Yukai Tu, Xiuquan Qiao, Guoshun Nan, Junliang Chen, Shasha Li
Content-Centric Networking (CCN) facilitates the content distribution for static Web applications due to its in-network caching. However, when it comes to dynamic Web request, the original fair scheduling of Interest packets on the CCN Web server (i.e. without differentiating a new service request and the service requests being processed) increases the mean response time, resulting in a decrease of service performance. To address this problem, we present a Priority-based Dynamic Web Requests Scheduling (called PBDRS) for CCN Web server. We first increase the priority of the subsequent Interest packet requests belonging to dynamic Web requests being processed, based on the first-come-first-served strategy. Then, services with small-size response data are also scheduled to a higher priority, leveraging the shortest remaining processing time (SRPT) scheduling policy. To validate the proposed approach, we implemented the proposed PBDRS mechanism in our existing CCN Web server and conducted the performance evaluation experiments based on a real dataset crawled from three popular dynamic Web sites of China in a trace-driven way. Experimental results indicate that PBDRS outperforms existing fair scheduling approach of Interest packets in terms of the mean response time.
{"title":"A Priority-Based Dynamic Web Requests Scheduling for Web Servers over Content-Centric Networking","authors":"Yukai Tu, Xiuquan Qiao, Guoshun Nan, Junliang Chen, Shasha Li","doi":"10.1109/HotWeb.2015.15","DOIUrl":"https://doi.org/10.1109/HotWeb.2015.15","url":null,"abstract":"Content-Centric Networking (CCN) facilitates the content distribution for static Web applications due to its in-network caching. However, when it comes to dynamic Web request, the original fair scheduling of Interest packets on the CCN Web server (i.e. without differentiating a new service request and the service requests being processed) increases the mean response time, resulting in a decrease of service performance. To address this problem, we present a Priority-based Dynamic Web Requests Scheduling (called PBDRS) for CCN Web server. We first increase the priority of the subsequent Interest packet requests belonging to dynamic Web requests being processed, based on the first-come-first-served strategy. Then, services with small-size response data are also scheduled to a higher priority, leveraging the shortest remaining processing time (SRPT) scheduling policy. To validate the proposed approach, we implemented the proposed PBDRS mechanism in our existing CCN Web server and conducted the performance evaluation experiments based on a real dataset crawled from three popular dynamic Web sites of China in a trace-driven way. Experimental results indicate that PBDRS outperforms existing fair scheduling approach of Interest packets in terms of the mean response time.","PeriodicalId":252318,"journal":{"name":"2015 Third IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124458894","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}
Pengcheng Xiong, Xin He, Hakan Hacıgümüş, P. Shenoy
MapReduce is a popular choice for executing analytic workloads over large datasets on clusters of commodity machines. Due to the distributed nature of such systems, network resource bottlenecks can adversely affect performance, especially when multiple applications share the network. One of the significant barriers to reducing the occurrence and impact of such bottlenecks is the current separation between MapReduce and network management and routing. Fortunately, the emergence of software-defined networking (SDN) is removing the barriers to cooperation between Hadoop and the network. To explore the opportunity this creates, we focus on how we can use the capabilities of SDN to create a more collaborative relationship between MapReduce and the network underneath. We demonstrate the effectiveness of this collaboration through the implementation of and experiments with a system we call Cormorant. Experimental results show up to 38% improvement for analytic query performance, beyond the benefits achievable by independently optimizing MapReduce schedulers and network flow schedulers.
{"title":"Cormorant: Running Analytic Queries on MapReduce with Collaborative Software-Defined Networking","authors":"Pengcheng Xiong, Xin He, Hakan Hacıgümüş, P. Shenoy","doi":"10.1109/HotWeb.2015.10","DOIUrl":"https://doi.org/10.1109/HotWeb.2015.10","url":null,"abstract":"MapReduce is a popular choice for executing analytic workloads over large datasets on clusters of commodity machines. Due to the distributed nature of such systems, network resource bottlenecks can adversely affect performance, especially when multiple applications share the network. One of the significant barriers to reducing the occurrence and impact of such bottlenecks is the current separation between MapReduce and network management and routing. Fortunately, the emergence of software-defined networking (SDN) is removing the barriers to cooperation between Hadoop and the network. To explore the opportunity this creates, we focus on how we can use the capabilities of SDN to create a more collaborative relationship between MapReduce and the network underneath. We demonstrate the effectiveness of this collaboration through the implementation of and experiments with a system we call Cormorant. Experimental results show up to 38% improvement for analytic query performance, beyond the benefits achievable by independently optimizing MapReduce schedulers and network flow schedulers.","PeriodicalId":252318,"journal":{"name":"2015 Third IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134571691","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}
Stefanos Antaris, Despina Stasi, Mikael Högqvist, G. Pallis, M. Dikaiakos
Decentralized Online Social Networks (DOSNs) offer an alternative to the usual centralized solutions, that promises to preserve more control of a user's data to the user herself. An important aspect in the design of DOSNs is the selection of the Peer-to-Peer overlay network. We propose the use of an augmented Pastry-based overlay network design, so as to produce a topology that incorporates the social network. Our augmentation aims to reduce the number of overlay hops required for the communication between two users in the social network. We experimentally show that our approach reduces the number of overlay hops and the total latency between two socially connected users versus the standard Pastry overlay. Additionally, we compare our approach with Sprout, a Chord alternative, and show that in many cases we achieve similarly low latency, while maintaining a bounded number of links.
{"title":"A Socio-Aware Decentralized Topology Construction Protocol","authors":"Stefanos Antaris, Despina Stasi, Mikael Högqvist, G. Pallis, M. Dikaiakos","doi":"10.1109/HotWeb.2015.13","DOIUrl":"https://doi.org/10.1109/HotWeb.2015.13","url":null,"abstract":"Decentralized Online Social Networks (DOSNs) offer an alternative to the usual centralized solutions, that promises to preserve more control of a user's data to the user herself. An important aspect in the design of DOSNs is the selection of the Peer-to-Peer overlay network. We propose the use of an augmented Pastry-based overlay network design, so as to produce a topology that incorporates the social network. Our augmentation aims to reduce the number of overlay hops required for the communication between two users in the social network. We experimentally show that our approach reduces the number of overlay hops and the total latency between two socially connected users versus the standard Pastry overlay. Additionally, we compare our approach with Sprout, a Chord alternative, and show that in many cases we achieve similarly low latency, while maintaining a bounded number of links.","PeriodicalId":252318,"journal":{"name":"2015 Third IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132577357","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}
Software testing is indispensable to assure software quality. Traditional software testing techniques are insufficient for modern web applications due to their unique characteristics, such as highly dynamic generation of web pages and interactions with DOM elements. Meanwhile, existing web testing techniques are cumbersome to use in long-term maintenance and evolution because the tests are often brittle in the presence of fast changes to web pages. In this paper, we propose an automatic test generation technique for dynamic web applications. Our technique effectively decouples test code from web pages by automatically generating page objects. Based on page objects, our technique performs iterative feedback directed random test generation. Our evaluation on seven real-world web applications demonstrates that the proposed technique achieves reasonable code coverage (from 23.3% to 90.8%) and can effectively explore dynamically generated web pages (around 14.7 times more than the original static web pages), revealing hundreds of HTML issues and five real errors in the applications.
{"title":"Incremental Web Application Testing Using Page Object","authors":"Ting Yu, Lei Ma, Cheng Zhang","doi":"10.1109/HotWeb.2015.14","DOIUrl":"https://doi.org/10.1109/HotWeb.2015.14","url":null,"abstract":"Software testing is indispensable to assure software quality. Traditional software testing techniques are insufficient for modern web applications due to their unique characteristics, such as highly dynamic generation of web pages and interactions with DOM elements. Meanwhile, existing web testing techniques are cumbersome to use in long-term maintenance and evolution because the tests are often brittle in the presence of fast changes to web pages. In this paper, we propose an automatic test generation technique for dynamic web applications. Our technique effectively decouples test code from web pages by automatically generating page objects. Based on page objects, our technique performs iterative feedback directed random test generation. Our evaluation on seven real-world web applications demonstrates that the proposed technique achieves reasonable code coverage (from 23.3% to 90.8%) and can effectively explore dynamically generated web pages (around 14.7 times more than the original static web pages), revealing hundreds of HTML issues and five real errors in the applications.","PeriodicalId":252318,"journal":{"name":"2015 Third IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126478480","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}
Key value data sets of the form {(x, wx)} where wx > 0 are prevalent. Common queries over such data are segment f-statistics Q(f, H) = Σx∈H f(wx), specified for a segment H of the keys and a function f. Different choices of f correspond to count, sum, moments, capping, and threshold statistics. When the data set is large, we can compute a smaller sample from which we can quickly estimate statistics. A weighted sample of keys taken with respect to f(wx) provides estimates with statistically guaranteed quality for f-statistics. Such a sample S(f) can be used to estimate g-statistics for g ≠ f, but quality degrades with the disparity between g and f. In this paper we address applications that require quality estimates for a set F of different functions. A naive solution is to compute and work with a different sample S(f) for each f ∈ F. Instead, this can be achieved more effectively and seamlessly using a single multi-objective sample S(F) of a much smaller size. We review multi-objective sampling schemes and place them in our context of estimating f-statistics. We show that a multi-objective sample for F provides quality estimates for any f that is a positive linear combination of functions from F. We then establish a surprising and powerful result when the target set M is all monotone non-decreasing functions, noting that M includes most natural statistics. We provide efficient multi-objective sampling algorithms for M and show that a sample size of k ln n (where n is the number of active keys) provides the same estimation quality, for any f ∈ M, as a dedicated weighted sample of size k for f.
{"title":"Multi-objective Weighted Sampling","authors":"E. Cohen","doi":"10.1109/HotWeb.2015.8","DOIUrl":"https://doi.org/10.1109/HotWeb.2015.8","url":null,"abstract":"Key value data sets of the form {(x, wx)} where wx > 0 are prevalent. Common queries over such data are segment f-statistics Q(f, H) = Σx∈H f(wx), specified for a segment H of the keys and a function f. Different choices of f correspond to count, sum, moments, capping, and threshold statistics. When the data set is large, we can compute a smaller sample from which we can quickly estimate statistics. A weighted sample of keys taken with respect to f(wx) provides estimates with statistically guaranteed quality for f-statistics. Such a sample S(f) can be used to estimate g-statistics for g ≠ f, but quality degrades with the disparity between g and f. In this paper we address applications that require quality estimates for a set F of different functions. A naive solution is to compute and work with a different sample S(f) for each f ∈ F. Instead, this can be achieved more effectively and seamlessly using a single multi-objective sample S(F) of a much smaller size. We review multi-objective sampling schemes and place them in our context of estimating f-statistics. We show that a multi-objective sample for F provides quality estimates for any f that is a positive linear combination of functions from F. We then establish a surprising and powerful result when the target set M is all monotone non-decreasing functions, noting that M includes most natural statistics. We provide efficient multi-objective sampling algorithms for M and show that a sample size of k ln n (where n is the number of active keys) provides the same estimation quality, for any f ∈ M, as a dedicated weighted sample of size k for f.","PeriodicalId":252318,"journal":{"name":"2015 Third IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126661975","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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