This paper presents C-CLOUD, a democratic cloud infrastructure for renting computing resources includingnon-cloud resources (i.e. computing equipment not part of any cloud infrastructure, such as, PCs, laptops, enterprise servers and clusters). C-CLOUD enables enormous amount of surplus computing resources, in the range of hundreds of millions, to be rented out to cloud users. Such a sharing of resources allows resource owners to earn from idle resources, and cloud users to have a cost-efficient alternative to large cloud providers. Compared to existing approaches to sharing surplus resources, C-CLOUD has two key challenges: ensuring Service Level Agreement (SLA) and reliability of reservations made over heterogeneous resources, and providing appropriate mechanism to encourage sharing of resources. In this context, C-CLOUD introduces novel incentive mechanism that determines resourcerents parametrically based on their reliability and capability.
{"title":"C-Cloud: A Cost-Efficient Reliable Cloud of Surplus Computing Resources","authors":"P. Dutta, Tridib Mukherjee, V. Hegde, Sujit Gujar","doi":"10.1109/CLOUD.2014.152","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.152","url":null,"abstract":"This paper presents C-CLOUD, a democratic cloud infrastructure for renting computing resources includingnon-cloud resources (i.e. computing equipment not part of any cloud infrastructure, such as, PCs, laptops, enterprise servers and clusters). C-CLOUD enables enormous amount of surplus computing resources, in the range of hundreds of millions, to be rented out to cloud users. Such a sharing of resources allows resource owners to earn from idle resources, and cloud users to have a cost-efficient alternative to large cloud providers. Compared to existing approaches to sharing surplus resources, C-CLOUD has two key challenges: ensuring Service Level Agreement (SLA) and reliability of reservations made over heterogeneous resources, and providing appropriate mechanism to encourage sharing of resources. In this context, C-CLOUD introduces novel incentive mechanism that determines resourcerents parametrically based on their reliability and capability.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127728654","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}
G. Anastasi, E. Carlini, M. Coppola, Patrizio Dazzi
The broad diffusion of Cloud Computing has fostered the proliferation of a large number of cloud computing providers. The need of Cloud Brokers arises for helping consumers in discovering, considering and comparing services with different capabilities and offered by different providers. Also, consuming services exposed by different providers, when possible, may alleviate the vendor lock-in. While it can be straightforward to choose the best provider when deploying small and homogeneous applications, things get harder if the size and complexity of applications grow up. In this paper we propose a genetic approach for Cloud Brokering, focusing on finding Infrastructure-as-a-Service (IaaS) resources for satisfying Quality of Service (QoS) requirements of applications. We performed a set of experiments with an implementation of such broker. Results show that our broker can find near-optimal solutions even when dealing with hundreds of providers, trying at the same time to mitigate the vendor lock-in.
{"title":"QBROKAGE: A Genetic Approach for QoS Cloud Brokering","authors":"G. Anastasi, E. Carlini, M. Coppola, Patrizio Dazzi","doi":"10.1109/CLOUD.2014.49","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.49","url":null,"abstract":"The broad diffusion of Cloud Computing has fostered the proliferation of a large number of cloud computing providers. The need of Cloud Brokers arises for helping consumers in discovering, considering and comparing services with different capabilities and offered by different providers. Also, consuming services exposed by different providers, when possible, may alleviate the vendor lock-in. While it can be straightforward to choose the best provider when deploying small and homogeneous applications, things get harder if the size and complexity of applications grow up. In this paper we propose a genetic approach for Cloud Brokering, focusing on finding Infrastructure-as-a-Service (IaaS) resources for satisfying Quality of Service (QoS) requirements of applications. We performed a set of experiments with an implementation of such broker. Results show that our broker can find near-optimal solutions even when dealing with hundreds of providers, trying at the same time to mitigate the vendor lock-in.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121118045","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}
Networking and virtualization are two key building blocks of modern cloud computing. The energy consumption of physical machines has been carefully examined in the past research, including the impact of network traffic. When it comes with virtual machines, the inter-play between energy consumption and network traffic however becomes much more complicated. The traffic are now generated by and exchanged between virtual machines (VMs), which could reside in different physical machines with their respective network interface cards (NICs), or share the same physical machine. When multiple VMs share a physical NIC, their traffic can interfere with each other, causing extra overhead. Yet the VM's allocation can be dynamic and they can even migrated across physical machines, thereby changing the traffic pattern. These factors combined make the network traffic highly diverse and dynamic, so is the corresponding energy consumption. A close examination on the network traffic and energy consumption in virtualized environments is thus of need. In this paper, we present an initial measurement study on the interplay between energy consumption and network traffic in representative virtualization environments. Our study reveals a series of unique energy consumption patterns of the network traffic in this context. We show that state-of-the-art virtualization designs noticeably increase the demand of CPU resources when handling networked transactions, generating excessive interrupt requests with ceaselessly context switching, which in turn increases energy consumption. Even when the physical machine is in an idle state, the VM network transactions will will incur remarkable energy consumption. Furthermore, even with identical number of VMs and amount of traffic on a physical machine, the energy consumptions vary significantly with different VM allocation strategies. Our close examination pinpoints the root cause, and offers new angles to revisit the existing resource usage and energy consumption models, so as to optimize the service provisioning as well as virtual machine placement and migration.
{"title":"On the Interplay between Network Traffic and Energy Consumption in Virtualized Environment: An Empirical Study","authors":"Chi Xu, Ziyang Zhao, Haiyang Wang, Jiangchuan Liu","doi":"10.1109/CLOUD.2014.60","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.60","url":null,"abstract":"Networking and virtualization are two key building blocks of modern cloud computing. The energy consumption of physical machines has been carefully examined in the past research, including the impact of network traffic. When it comes with virtual machines, the inter-play between energy consumption and network traffic however becomes much more complicated. The traffic are now generated by and exchanged between virtual machines (VMs), which could reside in different physical machines with their respective network interface cards (NICs), or share the same physical machine. When multiple VMs share a physical NIC, their traffic can interfere with each other, causing extra overhead. Yet the VM's allocation can be dynamic and they can even migrated across physical machines, thereby changing the traffic pattern. These factors combined make the network traffic highly diverse and dynamic, so is the corresponding energy consumption. A close examination on the network traffic and energy consumption in virtualized environments is thus of need. In this paper, we present an initial measurement study on the interplay between energy consumption and network traffic in representative virtualization environments. Our study reveals a series of unique energy consumption patterns of the network traffic in this context. We show that state-of-the-art virtualization designs noticeably increase the demand of CPU resources when handling networked transactions, generating excessive interrupt requests with ceaselessly context switching, which in turn increases energy consumption. Even when the physical machine is in an idle state, the VM network transactions will will incur remarkable energy consumption. Furthermore, even with identical number of VMs and amount of traffic on a physical machine, the energy consumptions vary significantly with different VM allocation strategies. Our close examination pinpoints the root cause, and offers new angles to revisit the existing resource usage and energy consumption models, so as to optimize the service provisioning as well as virtual machine placement and migration.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122426597","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}
Rising trends in the number of customers turning to the cloud for their computing needs has made effective resource allocation imperative for cloud service providers. In order to maximize profits and reduce waste, providers have started to explore the role of oversubscribing cloud resources. However, the benefits of oversubscription in the cloud are not without inherent risks. This paper attempts to unveil the different incentives, risks, and techniques behind oversubscription in a cloud infrastructure. CloudSim is used to compare the generated revenue and performance of oversubscribed and non-oversubscribed datacenters. The idea of multi-class service levels used in other overbooked industries is implemented in simulations modeling a priority class of VMs that pay a higher price for better performance. Results show that oversubscription has the potential to increase datacenter revenue, but the benefit comes with the risk of degraded QoS.
{"title":"Simulating the Effects of Cloud-Based Oversubscription on Datacenter Revenues and Performance in Single and Multi-class Service Levels","authors":"Rachel Householder, Scott Arnold, Robert C. Green","doi":"10.1109/CLOUD.2014.81","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.81","url":null,"abstract":"Rising trends in the number of customers turning to the cloud for their computing needs has made effective resource allocation imperative for cloud service providers. In order to maximize profits and reduce waste, providers have started to explore the role of oversubscribing cloud resources. However, the benefits of oversubscription in the cloud are not without inherent risks. This paper attempts to unveil the different incentives, risks, and techniques behind oversubscription in a cloud infrastructure. CloudSim is used to compare the generated revenue and performance of oversubscribed and non-oversubscribed datacenters. The idea of multi-class service levels used in other overbooked industries is implemented in simulations modeling a priority class of VMs that pay a higher price for better performance. Results show that oversubscription has the potential to increase datacenter revenue, but the benefit comes with the risk of degraded QoS.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125933497","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}
This paper presents a new graph-coloring model for advance resource reservation with minimum energy consumption in heterogeneous IaaS cloud data centers. We start with an exact integer linear programming (ILP) formulation which generalizes the graph coloring problem and follow with a fast Energy Efficient Graph Pre-coloring (EEGP) heuristic to address the scalability and to reduce convergence times. The results of performance evaluation and comparisons of EEGP with our exact algorithm and the Haizea advance reservation (AR) algorithm demonstrate the efficiency of EEGP for the energy efficient advance resource reservation problem. Our proposed EEGP heuristic is shown to perform very close to optimal, to scale well with problem size and to achieve convergence times close to the simple and fast AR algorithm that is however suboptimal.
{"title":"Exact and Heuristic Graph-Coloring for Energy Efficient Advance Cloud Resource Reservation","authors":"Chaima Ghribi, D. Zeghlache","doi":"10.1109/CLOUD.2014.25","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.25","url":null,"abstract":"This paper presents a new graph-coloring model for advance resource reservation with minimum energy consumption in heterogeneous IaaS cloud data centers. We start with an exact integer linear programming (ILP) formulation which generalizes the graph coloring problem and follow with a fast Energy Efficient Graph Pre-coloring (EEGP) heuristic to address the scalability and to reduce convergence times. The results of performance evaluation and comparisons of EEGP with our exact algorithm and the Haizea advance reservation (AR) algorithm demonstrate the efficiency of EEGP for the energy efficient advance resource reservation problem. Our proposed EEGP heuristic is shown to perform very close to optimal, to scale well with problem size and to achieve convergence times close to the simple and fast AR algorithm that is however suboptimal.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130661014","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}
After clients outsource their data to the cloud, they will lose physical control of their data. Many schemes are proposed for clients to verify the integrity of their data. This paper considers a complementary problem: When a client claims that the server has lost their data, how can we be sure that the client is correct and honest about the loss? It is possible that the client's meta data is corrupted or the client is lying in order to blackmail the server. In addition, most previous work relies on sequential indices. However, the indices bring significant overhead to bind an index to each block. We propose to replace sequential indices with much flexible non-sequential {it coordinates}. The binding of coordinates to data blocks is performed through a Coordinate Merkle Hash Tree (CMHT). Based on CMHT, we can improve both the average and the worst-case update overhead by simplifying the updating algorithm.
{"title":"Enabling Non-repudiable Data Possession Verification in Cloud Storage Systems","authors":"Zhen Mo, Yian Zhou, Shigang Chen, Chengzhong Xu","doi":"10.1109/CLOUD.2014.40","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.40","url":null,"abstract":"After clients outsource their data to the cloud, they will lose physical control of their data. Many schemes are proposed for clients to verify the integrity of their data. This paper considers a complementary problem: When a client claims that the server has lost their data, how can we be sure that the client is correct and honest about the loss? It is possible that the client's meta data is corrupted or the client is lying in order to blackmail the server. In addition, most previous work relies on sequential indices. However, the indices bring significant overhead to bind an index to each block. We propose to replace sequential indices with much flexible non-sequential {it coordinates}. The binding of coordinates to data blocks is performed through a Coordinate Merkle Hash Tree (CMHT). Based on CMHT, we can improve both the average and the worst-case update overhead by simplifying the updating algorithm.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"204 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131773785","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}
Hamoun Ghanbari, Marin Litoiu, P. Pawluk, C. Barna
This paper presents a model and an algorithm for optimal service placement (OSP) of a set of N-tier software systems, subject to dynamic changes in the workload, Service Level Agreements (SLA), and administrator preferences. The objective function models the resources' cost, the service level agreements and the trashing cost. The optimization algorithm is predictive: its allocation or reallocation decisions are based not only on the current metrics but also on predicted evolution of the system. The solution of the optimization, in each step, is a set some service replicas to be added or removed from the available hosts. These deployment changes are optimal with regards to overall objectives defined over time. In addition, the optimization considers the restrictions imposed on the number of possible service migrations at each time interval. We present experimental results that show the effectiveness of our approach.
{"title":"Replica Placement in Cloud through Simple Stochastic Model Predictive Control","authors":"Hamoun Ghanbari, Marin Litoiu, P. Pawluk, C. Barna","doi":"10.1109/CLOUD.2014.21","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.21","url":null,"abstract":"This paper presents a model and an algorithm for optimal service placement (OSP) of a set of N-tier software systems, subject to dynamic changes in the workload, Service Level Agreements (SLA), and administrator preferences. The objective function models the resources' cost, the service level agreements and the trashing cost. The optimization algorithm is predictive: its allocation or reallocation decisions are based not only on the current metrics but also on predicted evolution of the system. The solution of the optimization, in each step, is a set some service replicas to be added or removed from the available hosts. These deployment changes are optimal with regards to overall objectives defined over time. In addition, the optimization considers the restrictions imposed on the number of possible service migrations at each time interval. We present experimental results that show the effectiveness of our approach.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132663070","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}
Virtualization techniques are the key in both public and private cloud computing environments. In such environments, multiple virtual instances are running on the same physical machine. The logical isolation between systems makes security assurance weaker than physically isolated systems. Thus, Virtual Machine Introspection techniques become essential to prevent the virtual system from being vulnerable to attacks. However, this technique breaks down the borders of the segregation between multiple tenants, which should be avoided in a public cloud computing environment. In this paper, we focus on building an encrypted Virtual Machine Introspection system, CryptVMI, to address the above concern, especially in a public cloud system. Our approach maintains a query handler on the management node to handle encrypted queries from user clients. We pass the query to the corresponding compute node that holds the virtual instance queried. The introspection application deployed on the compute node processes the query and acquires the encrypted results from the virtual instance for the user. This work shows our design and preliminary implementation of this system.
{"title":"CryptVMI: Encrypted Virtual Machine Introspection in the Cloud","authors":"Fangzhou Yao, R. Campbell","doi":"10.1109/CLOUD.2014.149","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.149","url":null,"abstract":"Virtualization techniques are the key in both public and private cloud computing environments. In such environments, multiple virtual instances are running on the same physical machine. The logical isolation between systems makes security assurance weaker than physically isolated systems. Thus, Virtual Machine Introspection techniques become essential to prevent the virtual system from being vulnerable to attacks. However, this technique breaks down the borders of the segregation between multiple tenants, which should be avoided in a public cloud computing environment. In this paper, we focus on building an encrypted Virtual Machine Introspection system, CryptVMI, to address the above concern, especially in a public cloud system. Our approach maintains a query handler on the management node to handle encrypted queries from user clients. We pass the query to the corresponding compute node that holds the virtual instance queried. The introspection application deployed on the compute node processes the query and acquires the encrypted results from the virtual instance for the user. This work shows our design and preliminary implementation of this system.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125114106","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}
Umesh Deshpande, Yang You, Danny Chan, Nilton Bila, Kartik Gopalan
Traditional metrics for live migration of virtual machines (VM) include total migration time, downtime, network overhead, and application degradation. In this paper, we introduce a new metric, "eviction time", defined as the time to evict the entire state of a VM from the source host. Eviction time determines how quickly the source host can be taken offline, or the freed resources re-purposed for other VMs. In traditional approaches for live VM migration, such as pre-copy and post-copy, eviction time is equal to the total migration time, because the source and destination hosts are coupled for the duration of the migration. Eviction time increases if the destination host is slow to receive the incoming VM, such as due to insufficient memory or network bandwidth, thus tying up the source host. We present a new approach, called "Scatter-Gather" live migration, which reduces the eviction time when the destination host is resource constrained. The key idea is to decouple the source and the destination hosts. The source scatters the VM's memory state quickly to multiple intermediaries (hosts or middleboxes) in the cluster. Concurrently, the destination gathers the VM's memory from the intermediaries using a variant of post-copy VM migration. We have implemented a prototype of Scatter-Gather in the KVM/QEMU platform. In our evaluations, Scatter-Gather reduces the VM eviction time by up to a factor of 6 while maintaining comparable total migration time against traditional pre-copy and post-copy for a resource constrained destination.
{"title":"Fast Server Deprovisioning through Scatter-Gather Live Migration of Virtual Machines","authors":"Umesh Deshpande, Yang You, Danny Chan, Nilton Bila, Kartik Gopalan","doi":"10.1109/CLOUD.2014.58","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.58","url":null,"abstract":"Traditional metrics for live migration of virtual machines (VM) include total migration time, downtime, network overhead, and application degradation. In this paper, we introduce a new metric, \"eviction time\", defined as the time to evict the entire state of a VM from the source host. Eviction time determines how quickly the source host can be taken offline, or the freed resources re-purposed for other VMs. In traditional approaches for live VM migration, such as pre-copy and post-copy, eviction time is equal to the total migration time, because the source and destination hosts are coupled for the duration of the migration. Eviction time increases if the destination host is slow to receive the incoming VM, such as due to insufficient memory or network bandwidth, thus tying up the source host. We present a new approach, called \"Scatter-Gather\" live migration, which reduces the eviction time when the destination host is resource constrained. The key idea is to decouple the source and the destination hosts. The source scatters the VM's memory state quickly to multiple intermediaries (hosts or middleboxes) in the cluster. Concurrently, the destination gathers the VM's memory from the intermediaries using a variant of post-copy VM migration. We have implemented a prototype of Scatter-Gather in the KVM/QEMU platform. In our evaluations, Scatter-Gather reduces the VM eviction time by up to a factor of 6 while maintaining comparable total migration time against traditional pre-copy and post-copy for a resource constrained destination.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115923892","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}
J. L. Lucas-Simarro, R. Moreno-Vozmediano, F. Desprez, Jonathan Rouzaud-Cornabas
Nowadays, Clouds are used to host a large range of services. But between different Cloud Service Providers, the pricing model and the price of individual resources can be very different. Furthermore hosting a service in one Cloud is the major cause of service outage. To increase resiliency and minimize the monetary cost of running a service, it becomes mandatory to span it between different Clouds. Moreover, due to dynamicity of both the service and Clouds, it could be required to migrate a service at run time. Accordingly, this ability must be integrated into the multi-Cloud resource manager, i.e. the Cloud broker. But, when migrating a VM to a new Cloud Service Provider, the VM disk image has to be migrated too. Accordingly, data storage and transfer must be taken into account when choosing if and where an application will be migrated. In this paper, we extend a cost-optimization algorithm to take into account storage costs to approximate the optimal placement of a service. The data storage management consists in taking two decisions: the location of the upload of an image, and keep it on-line during the experiment lifetime or delete it when unused. Based on our experimentations, we show that the storage cost of VM disk image must not be neglected as it was done in previous works. Moreover, we show that using the accurate combinations of storage policies can dramatically reduce the storage cost (from 90% to 14% of the total bill).
{"title":"Image Transfer and Storage Cost Aware Brokering Strategies for Multiple Clouds","authors":"J. L. Lucas-Simarro, R. Moreno-Vozmediano, F. Desprez, Jonathan Rouzaud-Cornabas","doi":"10.1109/CLOUD.2014.103","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.103","url":null,"abstract":"Nowadays, Clouds are used to host a large range of services. But between different Cloud Service Providers, the pricing model and the price of individual resources can be very different. Furthermore hosting a service in one Cloud is the major cause of service outage. To increase resiliency and minimize the monetary cost of running a service, it becomes mandatory to span it between different Clouds. Moreover, due to dynamicity of both the service and Clouds, it could be required to migrate a service at run time. Accordingly, this ability must be integrated into the multi-Cloud resource manager, i.e. the Cloud broker. But, when migrating a VM to a new Cloud Service Provider, the VM disk image has to be migrated too. Accordingly, data storage and transfer must be taken into account when choosing if and where an application will be migrated. In this paper, we extend a cost-optimization algorithm to take into account storage costs to approximate the optimal placement of a service. The data storage management consists in taking two decisions: the location of the upload of an image, and keep it on-line during the experiment lifetime or delete it when unused. Based on our experimentations, we show that the storage cost of VM disk image must not be neglected as it was done in previous works. Moreover, we show that using the accurate combinations of storage policies can dramatically reduce the storage cost (from 90% to 14% of the total bill).","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"468 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124383893","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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