{"title":"Intercloud Networks Performance Analysis","authors":"M. McGrath, Patrick Raycroft, P. Brenner","doi":"10.1109/IC2E.2015.85","DOIUrl":"https://doi.org/10.1109/IC2E.2015.85","url":null,"abstract":"Continued growth in cloud IaaS adoption motivates greater transparency into IaaS network performance to effectively leverage intra and intercloud service and data migration needs of a global consumer base. In this work, we produce a baseline set of network performance measurements for cloud WANs both internal to individual IaaS provider's globally distributed datacenters and in between those provider's private infrastructures. Standard virtual machine instances provided by major IaaS vendors were utilized along with traditional open source operating systems and latency/bandwidth testing toolsets. The measurements provide insights into bandwidth and latency bottlenecks relative to global data transit between commercial production cloud resources. This data provides network engineers, IaaS/PaaS/SaaS providers, cloud service consumers, and intercloud testbed developers essential reference data upon which to set performance and QoS expectations and propose next generation cloud network infrastructure and software enhancements. Further, the evolution of baseline network metrics for cloud services helps provide uniformity and clarity to IaaS vendor performance and capability comparisons.","PeriodicalId":395715,"journal":{"name":"2015 IEEE International Conference on Cloud Engineering","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114922025","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":"Apache Storm, a Hands on Tutorial","authors":"R. Evans","doi":"10.1109/IC2E.2015.67","DOIUrl":"https://doi.org/10.1109/IC2E.2015.67","url":null,"abstract":"Summary form only given. Apache Storm is a popular low latency distributed stream processing framework. Apache Storm is used everywhere at Yahoo and at many other companies from automatically tagging every image uploaded to Flickr and analyzing trending search queries to monitoring production servers looking for problems. This hands-on tutorial is divided into two parts. The first part covers the basics of Storm, its architecture, and walks you through writing a simple application (not just word count). The second part looks more at how to modify Storm and will walk you through adding in a new feature.","PeriodicalId":395715,"journal":{"name":"2015 IEEE International Conference on Cloud Engineering","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114799379","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}
Peter Desnoyers, Jason Hennessey, Brent Holden, O. Krieger, L. Rudolph, Adam Young
{"title":"Using Open Stack for an Open Cloud Exchange(OCX)","authors":"Peter Desnoyers, Jason Hennessey, Brent Holden, O. Krieger, L. Rudolph, Adam Young","doi":"10.1109/IC2E.2015.40","DOIUrl":"https://doi.org/10.1109/IC2E.2015.40","url":null,"abstract":"We are developing a new public cloud, the Massachusetts Open Cloud (MOC) based on the model of an Open Cloud exchange (OCX). We discuss in this paper the vision of an OCX and how we intend to realize it using the Open Stack open-source cloud platform in the MOC. A limited form of an OCX can be achieved today by layering new services on top of Open Stack. We have performed an analysis of Open Stack to determine the changes needed in order to fully realize the OCX model. We describe these proposed changes, which although significant and requiring broad community involvement will provide functionality of value to both existing single-provider clouds as well as future multi-provider ones.","PeriodicalId":395715,"journal":{"name":"2015 IEEE International Conference on Cloud Engineering","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133827652","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":"Smart Cache: An Optimized MapReduce Implementation of Frequent Itemset Mining","authors":"Dachuan Huang, Yang Song, R. Routray, Feng Qin","doi":"10.1109/IC2E.2015.12","DOIUrl":"https://doi.org/10.1109/IC2E.2015.12","url":null,"abstract":"Frequent Item set Mining (FIM) is a classic data mining topic with many real world applications such as market basket analysis. Many algorithms including Apriori, FP-Growth, and Eclat were proposed in the FIM field. As the dataset size grows, researchers have proposed MapReduce version of FIM algorithms to meet the big data challenge. This paper proposes new improvements to the MapReduce implementation of FIM algorithm by introducing a cache layer and a selective online analyzer. We have evaluated the effectiveness and efficiency of Smart Cache via extensive experiments on four public datasets. Smart Cache can reduce on average 45.4%, and up to 97.0% of the total execution time compared with the state-of-the-art solution.","PeriodicalId":395715,"journal":{"name":"2015 IEEE International Conference on Cloud Engineering","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133902689","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 Possible Solution for Privacy Preserving Cloud Data Storage","authors":"Mitch Paul Mithun, C. Collberg, Derek E. Bambauer","doi":"10.1109/IC2E.2015.103","DOIUrl":"https://doi.org/10.1109/IC2E.2015.103","url":null,"abstract":"Despite the economic advantages of cloud data storage, many corporations have not yet migrated to this technology. While corporations in the financial sector cite data security as a reason, corporations in other sectors cite privacy concerns for this reluctance. In this paper, we propose a possible solution for this problem inspired by the HIPAA safe harbor methodology for data anonymization. The proposed technique involves using a hash function that uniquely identifies the data and then splitting data across multiple cloud providers. We propose that such a \"Good Enough\" approach to privacy-preserving cloud data storage is both technologically feasible and financially advantageous. Following this approach addresses concerns about privacy harms resulting from accidental or deliberate data spills from cloud providers. The \"Good Enough\" method will enable firms to move their data into the cloud without incurring privacy risks, enabling them to realize the economic advantages provided by the pay-per-use model of cloud data storage.","PeriodicalId":395715,"journal":{"name":"2015 IEEE International Conference on Cloud Engineering","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123969999","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}
Judicael A. Zounmevo, Swann Perarnau, K. Iskra, Kazutomo Yoshii, R. Gioiosa, B. V. Essen, M. Gokhale, E. León
{"title":"A Container-Based Approach to OS Specialization for Exascale Computing","authors":"Judicael A. Zounmevo, Swann Perarnau, K. Iskra, Kazutomo Yoshii, R. Gioiosa, B. V. Essen, M. Gokhale, E. León","doi":"10.1109/IC2E.2015.78","DOIUrl":"https://doi.org/10.1109/IC2E.2015.78","url":null,"abstract":"Future exascale systems will impose several conflicting challenges on the operating system (OS) running on the compute nodes of such machines. On the one hand, the targeted extreme scale requires the kind of high resource usage efficiency that is best provided by lightweight OSes. At the same time, substantial changes in hardware are expected for exascale systems. Compute nodes are expected to host a mix of general-purpose and special-purpose processors or accelerators tailored for serial, parallel, compute-intensive, or I/O-intensive workloads. Similarly, the deeper and more complex memory hierarchy will expose multiple coherence domains and NUMA nodes in addition to incorporating nonvolatile RAM. That expected workload and hardware heterogeneity and complexity is not compatible with the simplicity that characterizes high performance lightweight kernels. In this work, we describe the Argo Exascale node OS, which is our approach to providing in a single kernel the required OS environments for the two aforementioned conflicting goals. We resort to multiple OS specializations on top of a single Linux kernel coupled with multiple containers.","PeriodicalId":395715,"journal":{"name":"2015 IEEE International Conference on Cloud Engineering","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123061912","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}
Kaveh Razavi, Ana-Maria Ion, Genc Tato, Kyu-Young Jeong, R. Figueiredo, G. Pierre, T. Kielmann
{"title":"Kangaroo: A Tenant-Centric Software-Defined Cloud Infrastructure","authors":"Kaveh Razavi, Ana-Maria Ion, Genc Tato, Kyu-Young Jeong, R. Figueiredo, G. Pierre, T. Kielmann","doi":"10.1109/IC2E.2015.19","DOIUrl":"https://doi.org/10.1109/IC2E.2015.19","url":null,"abstract":"Applications on cloud infrastructures acquire virtual machines (VMs) from providers when necessary. The current interface for acquiring VMs from most providers, however, is too limiting for the tenants, in terms of granularity in which VMs can be acquired (e.g., small, medium, large, etc.), while giving very limited control over their placement. The former leads to VM underutilization, and the latter has performance implications, both translating into higher costs for the tenants. In this work, we leverage nested virtualization and a networking overlay to tackle these problems. We present Kangaroo, an Open Stack-based virtual infrastructure provider, and IPOPsm, a virtual networking switch for communication between nested VMs over different infrastructure VMs. In addition, we design and implement Skippy, the realization of our proposed virtual infrastructure API for programming Kangaroo. Our benchmarks show that through careful mapping of nested VMs to infrastructure VMs, Kangaroo achieves up to an order of magnitude better performance, with only half the cost on Amazon EC2. Further, Kangaroo's unified Open Stack API allows us to migrate an entire application between Amazon EC2 and our local Open Nebula deployment within a few minutes, without any downtime or modification to the application code.","PeriodicalId":395715,"journal":{"name":"2015 IEEE International Conference on Cloud Engineering","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132003347","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 Expressive Simulator for Dynamic Network Flows","authors":"P. Kathiravelu, L. Veiga","doi":"10.1109/IC2E.2015.43","DOIUrl":"https://doi.org/10.1109/IC2E.2015.43","url":null,"abstract":"Software-defined networking (SDN) increases the network programmability, promoting an effective development of networked systems of cloud scale. As the scale of the networks and systems is growing larger and larger with time, programmability of the systems and networks is researched intensively. Many emulators are proposed and implemented to emulate large and complex networks inside a single computer, or a cluster of computers in the research lab. However, the emulators lack the ability to represent large systems such as data center networks or content delivery networks. Many of the network algorithms and design choices can also be tested for their functionality and efficiency in a simulator environment. While network emulators and simulators exist, a generic network flow simulator that is easy to program a variety of highly distributed and gigantic systems is still lacking. This paper presents xSDN, an expressive simulator for dynamic network flows. Adhering to the principles of software-defined networking paradigm from the design, xSDN focuses to be lean, light-weight, easy to learn and configure, and efficient, that can simulate networks of a scale of million nodes within a few seconds.","PeriodicalId":395715,"journal":{"name":"2015 IEEE International Conference on Cloud Engineering","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132351528","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":"The Social Internet of Things","authors":"A. Iera, Giacomo Morabito, L. Atzori","doi":"10.1109/IC2E.2015.68","DOIUrl":"https://doi.org/10.1109/IC2E.2015.68","url":null,"abstract":"Summary form only given. All market and technology studies forecast an explosive growth in the number of \"things\" that will be connected to the Internet. The resulting network is what is commonly known as the \"Internet of Things\" (IoT). When compared to the traditional Internet, the extremely high complexity of the IoT environments (usually characterized by a huge number of nodes, high heterogeneity of their resources and capabilities, uncertainty on their trustworthiness, etc.) poses new challenges that cannot be faced by even very smart objects singularly.Social behavior is the answer found by several creatures to face the complexity of the surrounding environment. Accordingly, the concept of Social Internet of Things (SIoT) has been recently introduced and is the subject of a rapidly increasing research effort.During the tutorial the introduction of social notions into the IoT will be motivated, the basic concepts of the SIoT paradigm explained, and the existing related research results and industrial experimentations surveyed. Besides, the architecture of a sample SIoT-based platform will be detailed together with some exemplary applications. Reference web site for this tutorial: http://www.social-iot.org.","PeriodicalId":395715,"journal":{"name":"2015 IEEE International Conference on Cloud Engineering","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123740292","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 Case Study of IaaS and SaaS in a Public Cloud","authors":"Yasuhiro Yamasaki, M. Aritsugi","doi":"10.1109/IC2E.2015.54","DOIUrl":"https://doi.org/10.1109/IC2E.2015.54","url":null,"abstract":"In recent years many public clouds have been available, and not only Infrastructure as a Service (IaaS) but others including Software as a Service (SaaS) as well have been provided by them. We focus on Amazon EC2 and RDS as examples of IaaS and SaaS in a public cloud, respectively, and experimentally compare them in terms of performance and costs in this paper. The results would be helpful for the users in considering which platform they should use for their applications.","PeriodicalId":395715,"journal":{"name":"2015 IEEE International Conference on Cloud Engineering","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114307873","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}