The Biological applications such as Gene and Protein analysis integrate and analyze biological data for the research in many bioinformatics and other bio related fields. Such applications are used under many large scale scientific applications and help in computing, integrating data, execute the analysis, automate the process by using information retrieved by different tasks and computational procedures to assist the scientists in scientific discovery and data distribution. Grid based and/or web based scientific workflow tools are used for bioinformatics related complex research to make scientists’ and researchers’ work easier. On average, scientists spend about 80% of their time assembling data to prepare for analysis. This is due largely in part to the fact that many of these resources required for data processing must be gathered from an external source. The best of these resources, however, are scattered across the globe. They are hosted at universities, institutes, and laboratories throughout the world. To bring all of these resources together by hiding system, network, and application level heterogeneity issues are challenging.
{"title":"Cloud Computing Infrastructure for Biological Echo-Systems","authors":"Janaka Balasooriya","doi":"10.1109/CLOUD.2010.80","DOIUrl":"https://doi.org/10.1109/CLOUD.2010.80","url":null,"abstract":"The Biological applications such as Gene and Protein analysis integrate and analyze biological data for the research in many bioinformatics and other bio related fields. Such applications are used under many large scale scientific applications and help in computing, integrating data, execute the analysis, automate the process by using information retrieved by different tasks and computational procedures to assist the scientists in scientific discovery and data distribution. Grid based and/or web based scientific workflow tools are used for bioinformatics related complex research to make scientists’ and researchers’ work easier. On average, scientists spend about 80% of their time assembling data to prepare for analysis. This is due largely in part to the fact that many of these resources required for data processing must be gathered from an external source. The best of these resources, however, are scattered across the globe. They are hosted at universities, institutes, and laboratories throughout the world. To bring all of these resources together by hiding system, network, and application level heterogeneity issues are challenging.","PeriodicalId":375404,"journal":{"name":"2010 IEEE 3rd International Conference on Cloud Computing","volume":"215 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122424858","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}
Michael R. Head, A. Sailer, Hidayatullah Shaikh, D. Shea
Acquiring a private computing cloud is the first step that an enterprise would choose to enable the cloud model and get its considerable benefits while keeping the control within the enterprise. The enterprise level applications that provide the infrastructure enabling cloud computing services are typically built by integrating inter-related complex software components. Critical challenges of these applications are the increasing level of inter-component dependencies and the customized growth, which make recurrent deployment of such applications, as the one required in private clouds, labor intensive and error prone. In this paper we investigate the type of issues faced when deploying a cloud computing management infrastructure and propose a solution to self-assist the deployment. We show how by leveraging virtual image technologies we can detect faulty installations and their signatures early in the deployment process. We also propose a methodology to capture in a shared repository and update these signatures for reuse in subsequent deployments in the form of two level signature patterns. We explore the perspective of our solution and criteria of analysis.
{"title":"Towards Self-Assisted Troubleshooting for the Deployment of Private Clouds","authors":"Michael R. Head, A. Sailer, Hidayatullah Shaikh, D. Shea","doi":"10.1109/CLOUD.2010.12","DOIUrl":"https://doi.org/10.1109/CLOUD.2010.12","url":null,"abstract":"Acquiring a private computing cloud is the first step that an enterprise would choose to enable the cloud model and get its considerable benefits while keeping the control within the enterprise. The enterprise level applications that provide the infrastructure enabling cloud computing services are typically built by integrating inter-related complex software components. Critical challenges of these applications are the increasing level of inter-component dependencies and the customized growth, which make recurrent deployment of such applications, as the one required in private clouds, labor intensive and error prone. In this paper we investigate the type of issues faced when deploying a cloud computing management infrastructure and propose a solution to self-assist the deployment. We show how by leveraging virtual image technologies we can detect faulty installations and their signatures early in the deployment process. We also propose a methodology to capture in a shared repository and update these signatures for reuse in subsequent deployments in the form of two level signature patterns. We explore the perspective of our solution and criteria of analysis.","PeriodicalId":375404,"journal":{"name":"2010 IEEE 3rd International Conference on Cloud Computing","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122738546","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}
Ahmed Mihoob, Carlos Molina-Jiménez, S. Shrivastava
A pay–per–use cloud service should be made available to consumers with an unambiguous resource accounting model that precisely describes all the factors that are taken into account in calculating resource consumption charges. The paper proposes the notion of consumer–centric resource accounting model such that consumers can programmatically compute their consumption charges of a remotely used service. In particular, the notion of strongly consumer–centric accounting model is proposed that requires that all the data needed for calculating billing charges can be collected independently by the consumer (or a trusted third party, TTP); in effect, this means that a consumer (or a TTP) should be in a position to run their own measurement service. Strongly consumer–centric accounting models have the desirable property of openness and transparency, since service users are in a position to verify the charges billed to them. To illustrate the ideas, the accounting model of a given cloud infrastructure service (simple storage service, S3 from Amazon) is evaluated. The exercise reveals some shortcomings which can be fixed as indicated in this paper to make Amazon’s model strongly consumer–centric. Service providers can learn from this evaluation study to re-examine their accounting models and perform any amendments
{"title":"A Case for Consumer–centric Resource Accounting Models","authors":"Ahmed Mihoob, Carlos Molina-Jiménez, S. Shrivastava","doi":"10.1109/CLOUD.2010.44","DOIUrl":"https://doi.org/10.1109/CLOUD.2010.44","url":null,"abstract":"A pay–per–use cloud service should be made available to consumers with an unambiguous resource accounting model that precisely describes all the factors that are taken into account in calculating resource consumption charges. The paper proposes the notion of consumer–centric resource accounting model such that consumers can programmatically compute their consumption charges of a remotely used service. In particular, the notion of strongly consumer–centric accounting model is proposed that requires that all the data needed for calculating billing charges can be collected independently by the consumer (or a trusted third party, TTP); in effect, this means that a consumer (or a TTP) should be in a position to run their own measurement service. Strongly consumer–centric accounting models have the desirable property of openness and transparency, since service users are in a position to verify the charges billed to them. To illustrate the ideas, the accounting model of a given cloud infrastructure service (simple storage service, S3 from Amazon) is evaluated. The exercise reveals some shortcomings which can be fixed as indicated in this paper to make Amazon’s model strongly consumer–centric. Service providers can learn from this evaluation study to re-examine their accounting models and perform any amendments","PeriodicalId":375404,"journal":{"name":"2010 IEEE 3rd International Conference on Cloud Computing","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124816354","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}
Dirk Habich, Wolfgang Lehner, Sebastian Richly, U. Assmann
The role of data analytics increases in several application domains to cope with the large amount of captured data. Generally, data analytics are data-intensive processes, whose efficient execution is a challenging task. Each process consists of a collection of related structured activities, where huge data sets have to be exchanged between several loosely coupled services. The implementation of such processes in a service-oriented environment offers some advantages, but the efficient realization of data flows is difficult. Therefore, we use this paper to propose a novel SOA-aware approach with a special focus on the data flow. The tight interaction of new cloud technologies with SOA technologies enables us to optimize the execution of data-intensive service applications by reducing the data exchange tasks to a minimum. Fundamentally, our core concept to optimize the data flows is found in data clouds. Moreover, we can exploit our approach to derive efficient process execution strategies regarding different optimization objectives for the data flows.
{"title":"Using Cloud Technologies to Optimize Data-Intensive Service Applications","authors":"Dirk Habich, Wolfgang Lehner, Sebastian Richly, U. Assmann","doi":"10.1109/CLOUD.2010.56","DOIUrl":"https://doi.org/10.1109/CLOUD.2010.56","url":null,"abstract":"The role of data analytics increases in several application domains to cope with the large amount of captured data. Generally, data analytics are data-intensive processes, whose efficient execution is a challenging task. Each process consists of a collection of related structured activities, where huge data sets have to be exchanged between several loosely coupled services. The implementation of such processes in a service-oriented environment offers some advantages, but the efficient realization of data flows is difficult. Therefore, we use this paper to propose a novel SOA-aware approach with a special focus on the data flow. The tight interaction of new cloud technologies with SOA technologies enables us to optimize the execution of data-intensive service applications by reducing the data exchange tasks to a minimum. Fundamentally, our core concept to optimize the data flows is found in data clouds. Moreover, we can exploit our approach to derive efficient process execution strategies regarding different optimization objectives for the data flows.","PeriodicalId":375404,"journal":{"name":"2010 IEEE 3rd International Conference on Cloud Computing","volume":"16 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124478137","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}
Service-oriented architecture (SOA) paradigm for orchestrating large-scale distributed applications offers significant cost savings by reusing existing services. However, the high irregularity of client requests and the distributed nature of the approach may deteriorate service response time and availability. Static replication of components in datacenters for accommodating load spikes requires proper resource planning and underutilizes the cloud infrastructure. Moreover, no service availability guarantees are offered in case of datacenter failures. In this paper, we propose a cost-efficient approach for dynamic and geographically-diverse replication of components in a cloud computing infrastructure that effectively adapts to load variations and offers service availability guarantees. In our virtual economy, components rent server resources and replicate, migrate or delete themselves according to self-optimizing strategies. We experimentally prove that such an approach outperforms in response time even full replication of the components in all servers, while offering service availability guarantees under failures.
{"title":"An Economic Approach for Scalable and Highly-Available Distributed Applications","authors":"N. Bonvin, Thanasis G. Papaioannou, K. Aberer","doi":"10.1109/CLOUD.2010.45","DOIUrl":"https://doi.org/10.1109/CLOUD.2010.45","url":null,"abstract":"Service-oriented architecture (SOA) paradigm for orchestrating large-scale distributed applications offers significant cost savings by reusing existing services. However, the high irregularity of client requests and the distributed nature of the approach may deteriorate service response time and availability. Static replication of components in datacenters for accommodating load spikes requires proper resource planning and underutilizes the cloud infrastructure. Moreover, no service availability guarantees are offered in case of datacenter failures. In this paper, we propose a cost-efficient approach for dynamic and geographically-diverse replication of components in a cloud computing infrastructure that effectively adapts to load variations and offers service availability guarantees. In our virtual economy, components rent server resources and replicate, migrate or delete themselves according to self-optimizing strategies. We experimentally prove that such an approach outperforms in response time even full replication of the components in all servers, while offering service availability guarantees under failures.","PeriodicalId":375404,"journal":{"name":"2010 IEEE 3rd International Conference on Cloud Computing","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126693982","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}
In private clouds that host many enterprise applications, scalable security management has become an important issue. In our previous work, we had developed integrated access control manager that manages access permissions to a large number of various resources using resource information provided by a resource information service. To improve performance of the resource information service, we introduced a resource information cache and a proactive cache update control method. To avoid overload of the management server due to updating cached information, the proposed method selects a part of cached information by content priority as an update target. In this work, we evaluated the query response time of the resource information service in a third-party enterprise system using the search queries issued during system operations by an administrator. The proposed method reduced average query response time by 35% compared to a conventional reactive update control method.
{"title":"Resource Information Cache Update Control for Scalable Access Control Management Systems","authors":"Kumiko Tadano, M. Kawato, F. Machida, Y. Maeno","doi":"10.1109/CLOUD.2010.79","DOIUrl":"https://doi.org/10.1109/CLOUD.2010.79","url":null,"abstract":"In private clouds that host many enterprise applications, scalable security management has become an important issue. In our previous work, we had developed integrated access control manager that manages access permissions to a large number of various resources using resource information provided by a resource information service. To improve performance of the resource information service, we introduced a resource information cache and a proactive cache update control method. To avoid overload of the management server due to updating cached information, the proposed method selects a part of cached information by content priority as an update target. In this work, we evaluated the query response time of the resource information service in a third-party enterprise system using the search queries issued during system operations by an administrator. The proposed method reduced average query response time by 35% compared to a conventional reactive update control method.","PeriodicalId":375404,"journal":{"name":"2010 IEEE 3rd International Conference on Cloud Computing","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128881821","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}
The new paradigm of cloud computing poses severe security risks to its adopters. In order to cope with these risks, appropriate taxonomies and classification criteria for attacks on cloud computing are required. In this work-in-progress paper we present one such taxonomy based on the notion of attack surfaces of the cloud computing scenario participants.
{"title":"Attack Surfaces: A Taxonomy for Attacks on Cloud Services","authors":"Nils Gruschka, Meiko Jensen","doi":"10.1109/CLOUD.2010.23","DOIUrl":"https://doi.org/10.1109/CLOUD.2010.23","url":null,"abstract":"The new paradigm of cloud computing poses severe security risks to its adopters. In order to cope with these risks, appropriate taxonomies and classification criteria for attacks on cloud computing are required. In this work-in-progress paper we present one such taxonomy based on the notion of attack surfaces of the cloud computing scenario participants.","PeriodicalId":375404,"journal":{"name":"2010 IEEE 3rd International Conference on Cloud Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129196240","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}
We observe two of the recent trends in information technology. Cloud Computing (CC) is widely accepted as an effective reuse paradigm. Mobile Computing with Mobile Internet Device (MID) such as iPhones and Android devices becomes a convenient alternative to personal computers by integrating mobility, communication, software functionality, and entertainment. Due to the resource limitations of MIDs, cloud services become an ideal alternative to software installed on MIDs. A key feature of MIDs is the capability of sensing users’ contexts such as location, acceleration, longitude, latitude and movement. Hence, it is tempting to configure and provide cloud services for the specific context sensed, such as location-specific Map service. In this paper, we present a framework for enabling context-aware mobile services. The framework enables tasks of capturing context, determining what context-specific adaptation is needed, tailoring candidate services for the context, and running the adapted service. The net result of context-aware services is for consumers to receive better services which fit to the current context of the consumers.
{"title":"A Conceptual Framework for Provisioning Context-aware Mobile Cloud Services","authors":"H. La, Soo Dong Kim","doi":"10.1109/CLOUD.2010.78","DOIUrl":"https://doi.org/10.1109/CLOUD.2010.78","url":null,"abstract":"We observe two of the recent trends in information technology. Cloud Computing (CC) is widely accepted as an effective reuse paradigm. Mobile Computing with Mobile Internet Device (MID) such as iPhones and Android devices becomes a convenient alternative to personal computers by integrating mobility, communication, software functionality, and entertainment. Due to the resource limitations of MIDs, cloud services become an ideal alternative to software installed on MIDs. A key feature of MIDs is the capability of sensing users’ contexts such as location, acceleration, longitude, latitude and movement. Hence, it is tempting to configure and provide cloud services for the specific context sensed, such as location-specific Map service. In this paper, we present a framework for enabling context-aware mobile services. The framework enables tasks of capturing context, determining what context-specific adaptation is needed, tailoring candidate services for the context, and running the adapted service. The net result of context-aware services is for consumers to receive better services which fit to the current context of the consumers.","PeriodicalId":375404,"journal":{"name":"2010 IEEE 3rd International Conference on Cloud Computing","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115785583","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}
Cloud platforms enable enterprises to lease computing power in the form of virtual machines. An important problem for such enterprise users is to understand how many and what kinds of virtual machines will be needed from clouds. We formulate demand for computing power and other resources as a resource allocation problem with multiplicity, where computations that have to be performed concurrently are represented as tasks and a later task can reuse resources released by an earlier task. We show that finding a minimized allocation is NP-complete. This paper presents an approximation algorithm with a proof of its approximation bound that can yield close to optimum solutions in polynomial time. Enterprise users can exploit the solution to reduce the leasing cost and amortize the administration overhead (e.g., setting up VPNs or configuring a cluster). Cloud providers may utilize the solution to share their resources among a larger number of users.
{"title":"Optimal Resource Allocation in Clouds","authors":"Fangzhe Chang, J. Ren, R. Viswanathan","doi":"10.1109/CLOUD.2010.38","DOIUrl":"https://doi.org/10.1109/CLOUD.2010.38","url":null,"abstract":"Cloud platforms enable enterprises to lease computing power in the form of virtual machines. An important problem for such enterprise users is to understand how many and what kinds of virtual machines will be needed from clouds. We formulate demand for computing power and other resources as a resource allocation problem with multiplicity, where computations that have to be performed concurrently are represented as tasks and a later task can reuse resources released by an earlier task. We show that finding a minimized allocation is NP-complete. This paper presents an approximation algorithm with a proof of its approximation bound that can yield close to optimum solutions in polynomial time. Enterprise users can exploit the solution to reduce the leasing cost and amortize the administration overhead (e.g., setting up VPNs or configuring a cluster). Cloud providers may utilize the solution to share their resources among a larger number of users.","PeriodicalId":375404,"journal":{"name":"2010 IEEE 3rd International Conference on Cloud Computing","volume":"58 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132815039","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}
Cloud providers, like Amazon, offer their data centers' computational and storage capacities for lease to paying customers. High electricity consumption, associated with running a data center, not only reflects on its carbon footprint, but also increases the costs of running the data center itself. This paper addresses the problem of maximizing the revenues of Cloud providers by trimming down their electricity costs. As a solution allocation policies which are based on the dynamic powering servers on and off are introduced and evaluated. The policies aim at satisfying the conflicting goals of maximizing the users' experience while minimizing the amount of consumed electricity. The results of numerical experiments and simulations are described, showing that the proposed scheme performs well under different traffic conditions.
{"title":"Maximizing Cloud Providers' Revenues via Energy Aware Allocation Policies","authors":"M. Mazzucco, D. Dyachuk, R. Deters","doi":"10.1109/CLOUD.2010.68","DOIUrl":"https://doi.org/10.1109/CLOUD.2010.68","url":null,"abstract":"Cloud providers, like Amazon, offer their data centers' computational and storage capacities for lease to paying customers. High electricity consumption, associated with running a data center, not only reflects on its carbon footprint, but also increases the costs of running the data center itself. This paper addresses the problem of maximizing the revenues of Cloud providers by trimming down their electricity costs. As a solution allocation policies which are based on the dynamic powering servers on and off are introduced and evaluated. The policies aim at satisfying the conflicting goals of maximizing the users' experience while minimizing the amount of consumed electricity. The results of numerical experiments and simulations are described, showing that the proposed scheme performs well under different traffic conditions.","PeriodicalId":375404,"journal":{"name":"2010 IEEE 3rd International Conference on Cloud Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130475835","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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