Pub Date : 2014-10-01DOI: 10.1109/CCEM.2014.7015487
Venkatesh Nuthula, N. R. Challa
With the emergence of Cloud Computing technology many enterprises started moving their applications to the cloud to gain the benefits of hosting applications online versus having to have physical hardware or build out infrastructure. This new technology trend presents new challenges to application developers to enable applications in the cloud. Building applications for the cloud requires a major paradigm shift and new thinking about the application design, system architecture and needs an emphasis on leveraging massive scale. Building scalable applications for the cloud requires solid engineering and design by addressing the Statelessness, Redundancy, Resiliency, Server failures, New database approach, security, fast-changing platforms and dealing with different frameworks. While cloud deployments can abstract developers from having to deal with infrastructure issues, developers can focus on innovation and business logic instead of worrying about plumbing and infrastructure such as the operating systems, hardware etc. This paper is targeted towards cloud application developers and architects who are responsible for developing brand new cloud applications as well as migrating existing applications to clouds. The focus of this paper is to highlight design principles and best practices applicable to application development in cloud environment.
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Pub Date : 2014-10-01DOI: 10.1109/CCEM.2014.7015482
R. Khanna, M. Ganguli, Ananth S. Narayan, R. Abhiram, P. Gupta
In a cloud service management environment, service level agreements (SLA) define the expectation of quality (Quality-of-Service) for managing performance loss in a given service-hosting environment comprising of a pool of compute resources. Typically, complexity of resource inter-dependencies in a server system often results to sub-optimal behaviors leading to performance loss. A well behaved model can anticipate the demand patterns and proactively react to the dynamic stresses in a timely and well optimized manner. Dynamic characterization methods can synthesize self-correcting workload fingerprint code-book that facilitates phase prediction to achieve continuous tuning through proactive workload-allocation and load-balancing. In this paper we introduce the methodology that facilitates the coordinated tuning of the system resources through phase-assisted dynamic characterization. We describe the method to develop a multi-variate phase model by learning and classifying the run-time behavior of workloads. We demonstrate the workload phase forecasting method using phase extraction using a combination of machine learning approach. Results show the new model is about 98% accurate in phase identification and 97.15% accurate in forecasting the compute demands.
{"title":"Autonomic Characterization of Workloads Using Workload Fingerprinting","authors":"R. Khanna, M. Ganguli, Ananth S. Narayan, R. Abhiram, P. Gupta","doi":"10.1109/CCEM.2014.7015482","DOIUrl":"https://doi.org/10.1109/CCEM.2014.7015482","url":null,"abstract":"In a cloud service management environment, service level agreements (SLA) define the expectation of quality (Quality-of-Service) for managing performance loss in a given service-hosting environment comprising of a pool of compute resources. Typically, complexity of resource inter-dependencies in a server system often results to sub-optimal behaviors leading to performance loss. A well behaved model can anticipate the demand patterns and proactively react to the dynamic stresses in a timely and well optimized manner. Dynamic characterization methods can synthesize self-correcting workload fingerprint code-book that facilitates phase prediction to achieve continuous tuning through proactive workload-allocation and load-balancing. In this paper we introduce the methodology that facilitates the coordinated tuning of the system resources through phase-assisted dynamic characterization. We describe the method to develop a multi-variate phase model by learning and classifying the run-time behavior of workloads. We demonstrate the workload phase forecasting method using phase extraction using a combination of machine learning approach. Results show the new model is about 98% accurate in phase identification and 97.15% accurate in forecasting the compute demands.","PeriodicalId":143177,"journal":{"name":"2014 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127246368","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 : 2014-10-01DOI: 10.1109/CCEM.2014.7015486
Pramod C. Mane, Abhay A. Ratnaparkhi
The idea of composite cloud service has been emerging to reduce negative impact of cloud bursting. The novel idea of composite cloud services is achieved by forming a dynamic cloud collaboration platform among cloud providers. An important prerequisite of dynamic collaborative cloud formation is to reduce the cost of infrastructure and prevent loss to business enterprises owing to cloud bursting. The major concern in dynamic cloud collaboration is to minimize conflict among cloud providers and ensure each provider''s benefit. In recent years several market based models have been proposed that deal with twofold objectives: First, conflict minimization among providers and Second, benefit maximization of the providers. However, existing combinatorial auction based market models that attempt to achieve dynamic cloud collaboration are computationally in efficient. In this paper we have proposed cloud partner matching algorithm to facilitate partner selection process. Our proposed cloud partner matching algorithm minimizes conflicts among cloud providers by mutual consent.
{"title":"Cloud Partner Selection Algorithm for Dynamic Cloud Collaboration","authors":"Pramod C. Mane, Abhay A. Ratnaparkhi","doi":"10.1109/CCEM.2014.7015486","DOIUrl":"https://doi.org/10.1109/CCEM.2014.7015486","url":null,"abstract":"The idea of composite cloud service has been emerging to reduce negative impact of cloud bursting. The novel idea of composite cloud services is achieved by forming a dynamic cloud collaboration platform among cloud providers. An important prerequisite of dynamic collaborative cloud formation is to reduce the cost of infrastructure and prevent loss to business enterprises owing to cloud bursting. The major concern in dynamic cloud collaboration is to minimize conflict among cloud providers and ensure each provider''s benefit. In recent years several market based models have been proposed that deal with twofold objectives: First, conflict minimization among providers and Second, benefit maximization of the providers. However, existing combinatorial auction based market models that attempt to achieve dynamic cloud collaboration are computationally in efficient. In this paper we have proposed cloud partner matching algorithm to facilitate partner selection process. Our proposed cloud partner matching algorithm minimizes conflicts among cloud providers by mutual consent.","PeriodicalId":143177,"journal":{"name":"2014 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130082308","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 : 2014-10-01DOI: 10.1109/CCEM.2014.7015491
Sudhir Goyal, S. Bawa, Bhupinder Singh
Nowadays, with the increased deployment of servers to facilitate high performance computing (HPC) for scientific and engineering applications lead to large consumption of energy. Cloud computing is a cost-effective solution, as it allows to host storage, computational and supported network services on a shared infrastructure of physical servers. However, the growing demand of cloud infrastructure among the IT companies is drastically increasing, by which data centers are drawing more energy. Energy efficient scheduling is one effective solution to streamline the resource usage as well as reduce the energy consumption. The proposed work in this paper demonstrates the resource allocation and makes an energy consumption analysis of Greedy, Round Robin and Power Aware Best Fit Decreasing scheduling algorithms on a private academic cloud. This paper provides an insight into the working of different scheduling scenarios for cloud computing and demonstrates the potential for the improvement of energy efficiency of PABFD algorithm under academic workload.
{"title":"Experimental Comparison of Three Scheduling Algorithms for Energy Efficiency in Cloud Computing","authors":"Sudhir Goyal, S. Bawa, Bhupinder Singh","doi":"10.1109/CCEM.2014.7015491","DOIUrl":"https://doi.org/10.1109/CCEM.2014.7015491","url":null,"abstract":"Nowadays, with the increased deployment of servers to facilitate high performance computing (HPC) for scientific and engineering applications lead to large consumption of energy. Cloud computing is a cost-effective solution, as it allows to host storage, computational and supported network services on a shared infrastructure of physical servers. However, the growing demand of cloud infrastructure among the IT companies is drastically increasing, by which data centers are drawing more energy. Energy efficient scheduling is one effective solution to streamline the resource usage as well as reduce the energy consumption. The proposed work in this paper demonstrates the resource allocation and makes an energy consumption analysis of Greedy, Round Robin and Power Aware Best Fit Decreasing scheduling algorithms on a private academic cloud. This paper provides an insight into the working of different scheduling scenarios for cloud computing and demonstrates the potential for the improvement of energy efficiency of PABFD algorithm under academic workload.","PeriodicalId":143177,"journal":{"name":"2014 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131788867","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 : 2014-08-12DOI: 10.1109/CCEM.2014.7015496
D. Mittal, Damandeep Kaur, A. Aggarwal
With the advancement in technology, industry, e-commerce and research a large amount of complex and pervasive digital data is being generated which is increasing at an exponential rate and often termed as big data. Traditional Data Storage systems are not able to handle Big Data and also analyzing the Big Data becomes a challenge and thus it cannot be handled by traditional analytic tools. Cloud Computing can resolve the problem of handling, storage and analyzing the Big Data as it distributes the big data within the cloudlets. No doubt, Cloud Computing is the best answer available to the problem of Big Data storage and its analyses but having said that, there is always a potential risk to the security of Big Data storage in Cloud Computing, which needs to be addressed. Data Privacy is one of the major issues while storing the Big Data in a Cloud environment. Data Mining based attacks, a major threat to the data, allows an adversary or an unauthorized user to infer valuable and sensitive information by analyzing the results generated from computation performed on the raw data. This thesis proposes a secure k-means data mining approach assuming the data to be distributed among different hosts preserving the privacy of the data. The approach is able to maintain the correctness and validity of the existing k-means to generate the final results even in the distributed environment.
{"title":"Secure Data Mining in Cloud Using Homomorphic Encryption","authors":"D. Mittal, Damandeep Kaur, A. Aggarwal","doi":"10.1109/CCEM.2014.7015496","DOIUrl":"https://doi.org/10.1109/CCEM.2014.7015496","url":null,"abstract":"With the advancement in technology, industry, e-commerce and research a large amount of complex and pervasive digital data is being generated which is increasing at an exponential rate and often termed as big data. Traditional Data Storage systems are not able to handle Big Data and also analyzing the Big Data becomes a challenge and thus it cannot be handled by traditional analytic tools. Cloud Computing can resolve the problem of handling, storage and analyzing the Big Data as it distributes the big data within the cloudlets. No doubt, Cloud Computing is the best answer available to the problem of Big Data storage and its analyses but having said that, there is always a potential risk to the security of Big Data storage in Cloud Computing, which needs to be addressed. Data Privacy is one of the major issues while storing the Big Data in a Cloud environment. Data Mining based attacks, a major threat to the data, allows an adversary or an unauthorized user to infer valuable and sensitive information by analyzing the results generated from computation performed on the raw data. This thesis proposes a secure k-means data mining approach assuming the data to be distributed among different hosts preserving the privacy of the data. The approach is able to maintain the correctness and validity of the existing k-means to generate the final results even in the distributed environment.","PeriodicalId":143177,"journal":{"name":"2014 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127218981","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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