Virtual machine (VM) placement is the process of selecting the most suitable server in large cloud data centers to deploy newly-created VMs. Several approaches have been proposed to find a solution to this problem. However, most of the existing solutions only consider a limited number of resource types, thus resulting in unbalanced load or in the unnecessary activation of physical servers. In this article, we propose an algorithm, called Max-BRU, that maximizes the resource utilization and balances the usage of resources across multiple dimensions. Our algorithm is based on multiple resource-constraint metrics that help to find the most suitable server for deploying VMs in large cloud data centers. The proposed Max-BRU algorithm is evaluated by simulations based on synthetic datasets. Experimental results show two major improvements over the existing approaches for VM placement. First, Max-BRU increases the resource utilization by minimizing the amount of physical servers used. Second, Max-BRU effectively balances the utilization of multiple types of resources.
{"title":"A virtual machine placement algorithm for balanced resource utilization in cloud data centers","authors":"N. Hieu, M. D. Francesco, Antti Ylä-Jääski","doi":"10.1109/CLOUD.2014.70","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.70","url":null,"abstract":"Virtual machine (VM) placement is the process of selecting the most suitable server in large cloud data centers to deploy newly-created VMs. Several approaches have been proposed to find a solution to this problem. However, most of the existing solutions only consider a limited number of resource types, thus resulting in unbalanced load or in the unnecessary activation of physical servers. In this article, we propose an algorithm, called Max-BRU, that maximizes the resource utilization and balances the usage of resources across multiple dimensions. Our algorithm is based on multiple resource-constraint metrics that help to find the most suitable server for deploying VMs in large cloud data centers. The proposed Max-BRU algorithm is evaluated by simulations based on synthetic datasets. Experimental results show two major improvements over the existing approaches for VM placement. First, Max-BRU increases the resource utilization by minimizing the amount of physical servers used. Second, Max-BRU effectively balances the utilization of multiple types of resources.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"11 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":"128709493","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 resource allocation and pricing is a significant and challenging problem for modern cloud providers, which needs to be addressed. In this work, we propose an adaptive greedy mechanism, which is a new type of greedy market mechanism for efficient cloud resource allocation. The mechanism is combinatorial and it is designed to be operated by a single cloud provider. We prove that our proposed market mechanism is truthful, i.e. the buyers do not have an incentive to lie about their true valuation for the resource. Our experimental investigation showed that the proposed mechanism outperforms the conventional (single-shot) approach for solving combinatorial auction in terms of generated social welfare and resource utilization.
{"title":"Adaptive Market Mechanism for Efficient Cloud Services Trading","authors":"S. Chichin, Quoc Bao Vo, R. Kowalczyk","doi":"10.1109/CLOUD.2014.99","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.99","url":null,"abstract":"Cloud resource allocation and pricing is a significant and challenging problem for modern cloud providers, which needs to be addressed. In this work, we propose an adaptive greedy mechanism, which is a new type of greedy market mechanism for efficient cloud resource allocation. The mechanism is combinatorial and it is designed to be operated by a single cloud provider. We prove that our proposed market mechanism is truthful, i.e. the buyers do not have an incentive to lie about their true valuation for the resource. Our experimental investigation showed that the proposed mechanism outperforms the conventional (single-shot) approach for solving combinatorial auction in terms of generated social welfare and resource utilization.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"138 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":"132391246","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}
Although cloud has been adopted by many organizations as their main infrastructure for IT delivery, there are still a large number of legacy applications running in non-cloud hosting environments. Thus, it is crucial to have migration techniques for such legacy applications so that they can benefit from many advantages of cloud such as elasticity, low upfront investment, and fast time-to-market. However, migrating large number of legacy applications into cloud in a timely manner is a daunting task. Common techniques such as redeveloping (i.e., modernizing) them or reinstalling from the scratch entails high costs. To mitigate these problems, we have developed a rapid migration technique, called AppCloak, that allows users to literally copy an already-installed application to cloud and run it without any modifications. The technique is based on intercepting a selected set of system calls and replacing the parameters and return values to hide any differences of environments to the application. We demonstrate that our technique works in Amazon EC2 and quantify the performance overhead.
{"title":"AppCloak: Rapid Migration of Legacy Applications into Cloud","authors":"Byungchul Tak, Chunqiang Tang","doi":"10.1109/CLOUD.2014.112","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.112","url":null,"abstract":"Although cloud has been adopted by many organizations as their main infrastructure for IT delivery, there are still a large number of legacy applications running in non-cloud hosting environments. Thus, it is crucial to have migration techniques for such legacy applications so that they can benefit from many advantages of cloud such as elasticity, low upfront investment, and fast time-to-market. However, migrating large number of legacy applications into cloud in a timely manner is a daunting task. Common techniques such as redeveloping (i.e., modernizing) them or reinstalling from the scratch entails high costs. To mitigate these problems, we have developed a rapid migration technique, called AppCloak, that allows users to literally copy an already-installed application to cloud and run it without any modifications. The technique is based on intercepting a selected set of system calls and replacing the parameters and return values to hide any differences of environments to the application. We demonstrate that our technique works in Amazon EC2 and quantify the performance overhead.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"153 1 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":"114066033","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}
Dynamic resource scaling is a key property of cloud computing. Users can acquire or release required capacity for their applications on-the-fly. The most widely used and practical approach for dynamic scaling based on predefined policies (rules). For example, IaaS providers such as RightScale asks application owners to manually set the scaling rules. This task assumes, that the user has an expertise knowledge about the application being run on the cloud. However, it is not always true. In this paper we propose a lightweight adaptive multi-tier scaling framework VscalerLight, which learns scaling policy online. Our framework performs fine-grained vertical resource scaling of multi-tier web application. We present the design and implementation of VscalerLight. We evaluate the framework against widely used RUBiS benchmark. Results show that the application under control of VscalerLight guarantees 95th percentile response time specified in SLA.
{"title":"Lightweight Automatic Resource Scaling for Multi-tier Web Applications","authors":"Lenar Yazdanov, C. Fetzer","doi":"10.1109/CLOUD.2014.69","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.69","url":null,"abstract":"Dynamic resource scaling is a key property of cloud computing. Users can acquire or release required capacity for their applications on-the-fly. The most widely used and practical approach for dynamic scaling based on predefined policies (rules). For example, IaaS providers such as RightScale asks application owners to manually set the scaling rules. This task assumes, that the user has an expertise knowledge about the application being run on the cloud. However, it is not always true. In this paper we propose a lightweight adaptive multi-tier scaling framework VscalerLight, which learns scaling policy online. Our framework performs fine-grained vertical resource scaling of multi-tier web application. We present the design and implementation of VscalerLight. We evaluate the framework against widely used RUBiS benchmark. Results show that the application under control of VscalerLight guarantees 95th percentile response time specified in SLA.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"48 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":"121651167","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}
Jinho Hwang, Wei Zhang, R. C. Chiang, Timothy Wood, H. Howie Huang
Application and OS-level caches are crucial for hiding I/O latency and improving application performance. However, caches are designed to greedily consume memory, which can cause memory-hogging problems in a virtualized data centers since the hypervisor cannot tell for what a virtual machine uses its memory. A group of virtual machines may contain a wide range of caches: database query pools, memcached key-value stores, disk caches, etc., each of which would like as much memory as possible. The relative importance of these caches can vary significantly, yet system administrators currently have no easy way to dynamically manage the resources assigned to a range of virtual machine data caches in a unified way. To improve this situation, we have developed UniCache, a system that provides a hypervisor managed volatile data store that can cache data either in hypervisor controlled main memory (hot data) or on Flash based storage (cold data). We propose a two-level cache management system that uses a combination of recency information, object size, and a prediction of the cost to recover an object to guide its eviction algorithm. We have built a prototype of UniCache using Xen, and have evaluated its effectiveness in a shared environment where multiple virtual machines compete for storage resources.
{"title":"UniCache: Hypervisor Managed Data Storage in RAM and Flash","authors":"Jinho Hwang, Wei Zhang, R. C. Chiang, Timothy Wood, H. Howie Huang","doi":"10.1109/CLOUD.2014.38","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.38","url":null,"abstract":"Application and OS-level caches are crucial for hiding I/O latency and improving application performance. However, caches are designed to greedily consume memory, which can cause memory-hogging problems in a virtualized data centers since the hypervisor cannot tell for what a virtual machine uses its memory. A group of virtual machines may contain a wide range of caches: database query pools, memcached key-value stores, disk caches, etc., each of which would like as much memory as possible. The relative importance of these caches can vary significantly, yet system administrators currently have no easy way to dynamically manage the resources assigned to a range of virtual machine data caches in a unified way. To improve this situation, we have developed UniCache, a system that provides a hypervisor managed volatile data store that can cache data either in hypervisor controlled main memory (hot data) or on Flash based storage (cold data). We propose a two-level cache management system that uses a combination of recency information, object size, and a prediction of the cost to recover an object to guide its eviction algorithm. We have built a prototype of UniCache using Xen, and have evaluated its effectiveness in a shared environment where multiple virtual machines compete for storage resources.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"91 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":"123781092","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}
C. Ardagna, E. Damiani, Fulvio Frati, Guido Montalbano, Davide Rebeccani, M. Ughetti
The success of cloud computing has radically changed the way in which services are implemented and deployed, and made accessible to external and remote users. The cloud computing paradigm, in fact, supports a vision of distributed IT where software services and applications are outsourced and used on a pay-as-you-go basis. In this context, the ability to guarantee an effective management of cloud performance and to support automatic scalability become fundamental requirements. Cloud users are increasingly interested in a transparent and coherent vision of cloud, where performance is guaranteed in different scenarios, and under different and heterogeneous loads. In this paper, we analyze the benefits of an integrated scalability approach at different layers of the cloud stack, focusing on the computing infrastructure and database layers. To this aim, we provide different performance metrics and a set of rules based on them to evaluate the status of the cloud stack and scale it on demand to maintain stable performance. We then implement a proof-of-concept architecture to experimentally analyze cloud performance in three scenarios of scalability: computing infrastructure only, database only, and the case in which computing infrastructure and database compete for resources.
{"title":"A Competitive Scalability Approach for Cloud Architectures","authors":"C. Ardagna, E. Damiani, Fulvio Frati, Guido Montalbano, Davide Rebeccani, M. Ughetti","doi":"10.1109/CLOUD.2014.87","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.87","url":null,"abstract":"The success of cloud computing has radically changed the way in which services are implemented and deployed, and made accessible to external and remote users. The cloud computing paradigm, in fact, supports a vision of distributed IT where software services and applications are outsourced and used on a pay-as-you-go basis. In this context, the ability to guarantee an effective management of cloud performance and to support automatic scalability become fundamental requirements. Cloud users are increasingly interested in a transparent and coherent vision of cloud, where performance is guaranteed in different scenarios, and under different and heterogeneous loads. In this paper, we analyze the benefits of an integrated scalability approach at different layers of the cloud stack, focusing on the computing infrastructure and database layers. To this aim, we provide different performance metrics and a set of rules based on them to evaluate the status of the cloud stack and scale it on demand to maintain stable performance. We then implement a proof-of-concept architecture to experimentally analyze cloud performance in three scenarios of scalability: computing infrastructure only, database only, and the case in which computing infrastructure and database compete for resources.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"29 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":"122919615","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-federations have emerged as popular platforms for Internet-scale services. Cloud-federations are running over multiple datacenters, because a cloud-federation is an aggregate of cloud services each of which runs in a single datacenter. In such inter-datacenter environments, distributed key-value stores (DKVSs) are attractive databases in terms of scalability. However, inter-datacenter communications degrade the performance of these DKVSs because of their large latency and narrow bandwidth. In this paper, we demonstrate how to reduce and hide the weak points of inter-datacenter communications for DKVSs. To solve the problems we introduce two techniques called multi-layered DHT (ML-DHT) and local-first data rebuilding (LDR). ML-DHT provides a global and consistent index of key-value pairs with the efficient expandability of the storage capacity. It employs a routing protocol which reduces routing hops that pass through interdatacenter connections. LDR reduces data transfer on interdatacenter connections by using erasure coding techniques. It enables KVS administrators to flexibly make trade-offs between expandability of storage capacity and the performance of data transfer. Experimental results demonstrate that our techniques improve the latency up to 74 % compared with a Chord-based system and enable us to balance the amount of storage usage and remote data transfer.
{"title":"Minimizing WAN Communications in Inter-datacenter Key-Value Stores","authors":"H. Horie, M. Asahara, H. Yamada, K. Kono","doi":"10.1109/CLOUD.2014.72","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.72","url":null,"abstract":"Cloud-federations have emerged as popular platforms for Internet-scale services. Cloud-federations are running over multiple datacenters, because a cloud-federation is an aggregate of cloud services each of which runs in a single datacenter. In such inter-datacenter environments, distributed key-value stores (DKVSs) are attractive databases in terms of scalability. However, inter-datacenter communications degrade the performance of these DKVSs because of their large latency and narrow bandwidth. In this paper, we demonstrate how to reduce and hide the weak points of inter-datacenter communications for DKVSs. To solve the problems we introduce two techniques called multi-layered DHT (ML-DHT) and local-first data rebuilding (LDR). ML-DHT provides a global and consistent index of key-value pairs with the efficient expandability of the storage capacity. It employs a routing protocol which reduces routing hops that pass through interdatacenter connections. LDR reduces data transfer on interdatacenter connections by using erasure coding techniques. It enables KVS administrators to flexibly make trade-offs between expandability of storage capacity and the performance of data transfer. Experimental results demonstrate that our techniques improve the latency up to 74 % compared with a Chord-based system and enable us to balance the amount of storage usage and remote data transfer.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"1 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":"117325335","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}
F. Farahnakian, T. Pahikkala, P. Liljeberg, J. Plosila
In order to resource management in a large-scale data center, we present a hierarchical agent-based architecture. In this architecture, multi agents cooperate together to minimize the number of active physical machines according to the current resource requirements. We proposed a local agent in each physical machine (PM) to determine the PM's status and a global agent to optimizes VM placement based on PM's status. Experimental results show the proposed architecture can minimize energy consumption while maintaining an acceptable QoS.
{"title":"Hierarchical Agent-Based Architecture for Resource Management in Cloud Data Centers","authors":"F. Farahnakian, T. Pahikkala, P. Liljeberg, J. Plosila","doi":"10.1109/CLOUD.2014.128","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.128","url":null,"abstract":"In order to resource management in a large-scale data center, we present a hierarchical agent-based architecture. In this architecture, multi agents cooperate together to minimize the number of active physical machines according to the current resource requirements. We proposed a local agent in each physical machine (PM) to determine the PM's status and a global agent to optimizes VM placement based on PM's status. Experimental results show the proposed architecture can minimize energy consumption while maintaining an acceptable QoS.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"4 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":"123921201","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}
Yanzhang He, Xiaohong Jiang, Zhaohui Wu, Kejiang Ye, Zhongzhong Chen
With the rapid development of big data and cloud computing, big data analytics as a service in the cloud is becoming increasingly popular. More and more individuals and organizations tend to rent virtual cluster to store and analyze data rather than building their own data centers. However, in virtualization environment, whether scaling out using a cluster with more nodes to process big data is better than scaling up by adding more resources to the original virtual machines (VMs) in cluster is not clear. In this paper, we study the scalability performance issues of hadoop virtual cluster with cost consideration. We first present the design and implementation of VirtualMR platform which can provide users with scalable hadoop virtual cluster services for the MapReduce based big data analytics. Then we run a series of hadoop benchmarks and real parallel machine learning algorithms to evaluate the scalability performance, including scale-up method and scale-out method. Finally, we integrate our platform with resource monitoring module and propose a system tuner. By analyzing the monitored data, we dynamically adjust the parameters of hadoop framework and virtual machine configuration to improve resource utilization and reduce rent cost. Experimental results show that the scale-up method outperforms the scale-out method for CPU-bound applications, and it is opposite for I/O-bound applications. The results also verify the efficiency of system tuner to increase resource utilization and reduce rent cost.
{"title":"Scalability Analysis and Improvement of Hadoop Virtual Cluster with Cost Consideration","authors":"Yanzhang He, Xiaohong Jiang, Zhaohui Wu, Kejiang Ye, Zhongzhong Chen","doi":"10.1109/CLOUD.2014.85","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.85","url":null,"abstract":"With the rapid development of big data and cloud computing, big data analytics as a service in the cloud is becoming increasingly popular. More and more individuals and organizations tend to rent virtual cluster to store and analyze data rather than building their own data centers. However, in virtualization environment, whether scaling out using a cluster with more nodes to process big data is better than scaling up by adding more resources to the original virtual machines (VMs) in cluster is not clear. In this paper, we study the scalability performance issues of hadoop virtual cluster with cost consideration. We first present the design and implementation of VirtualMR platform which can provide users with scalable hadoop virtual cluster services for the MapReduce based big data analytics. Then we run a series of hadoop benchmarks and real parallel machine learning algorithms to evaluate the scalability performance, including scale-up method and scale-out method. Finally, we integrate our platform with resource monitoring module and propose a system tuner. By analyzing the monitored data, we dynamically adjust the parameters of hadoop framework and virtual machine configuration to improve resource utilization and reduce rent cost. Experimental results show that the scale-up method outperforms the scale-out method for CPU-bound applications, and it is opposite for I/O-bound applications. The results also verify the efficiency of system tuner to increase resource utilization and reduce rent cost.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"16 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":"129491160","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}
A. Rezgui, Gary Quezada, M. M. Rafique, Zaki Malik
In volunteer cloud federations (VCFs), volunteers join and leave without restrictions and may collectively contribute a large number of heterogeneous virtual machine instances. A challenge is to efficiently allocate this dynamic, heterogeneous capacity to a flow of incoming virtual machine (VM) instantiation requests, i.e., maximize the number of virtual machines that may be placed on the VCF. Cloud federations may allocate VMs far more efficiently if they can accurately predict the demand in terms of VM instantiation requests. In this paper, we present a stochastic technique that forecasts future demand to efficiently allocate VMs to VM instantiation requests. Our approach uses a Markov Chain Monte Carlo (MCMC) simulation known as the Poisson-Gamma Gibbs (PGG) sampler. The PGG sampler is used to determine the arrival rate of each type of VM instantiation requests. This arrival rate is then used to determine an optimal VM placement for the incoming VM instantiation requests. We compared our approach to a solution that adopts a static smallest-fit approach. The experimental results showed that our solution reacts quickly to abrupt changes in the frequency of VM instantiation requests and performs 10% better than the static smallest-fit approach in terms of the total number of satisfied requests.
{"title":"A Capacity Allocation Approach for Volunteer Cloud Federations Using Poisson-Gamma Gibbs Sampling","authors":"A. Rezgui, Gary Quezada, M. M. Rafique, Zaki Malik","doi":"10.1109/CLOUD.2014.47","DOIUrl":"https://doi.org/10.1109/CLOUD.2014.47","url":null,"abstract":"In volunteer cloud federations (VCFs), volunteers join and leave without restrictions and may collectively contribute a large number of heterogeneous virtual machine instances. A challenge is to efficiently allocate this dynamic, heterogeneous capacity to a flow of incoming virtual machine (VM) instantiation requests, i.e., maximize the number of virtual machines that may be placed on the VCF. Cloud federations may allocate VMs far more efficiently if they can accurately predict the demand in terms of VM instantiation requests. In this paper, we present a stochastic technique that forecasts future demand to efficiently allocate VMs to VM instantiation requests. Our approach uses a Markov Chain Monte Carlo (MCMC) simulation known as the Poisson-Gamma Gibbs (PGG) sampler. The PGG sampler is used to determine the arrival rate of each type of VM instantiation requests. This arrival rate is then used to determine an optimal VM placement for the incoming VM instantiation requests. We compared our approach to a solution that adopts a static smallest-fit approach. The experimental results showed that our solution reacts quickly to abrupt changes in the frequency of VM instantiation requests and performs 10% better than the static smallest-fit approach in terms of the total number of satisfied requests.","PeriodicalId":288542,"journal":{"name":"2014 IEEE 7th International Conference on Cloud Computing","volume":"53 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":"124981206","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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