B. Solomon, D. Ionescu, C. Gadea, S. Veres, Marin Litoiu
In the past few years, cloud computing has become an integral technology both for the day to day running of corporations, as well as in everyday life as more services are offered which use a backend cloud. At the same time online collaboration tools are becoming more important as both businesses and individuals need to share information and collaborate with other entities. Previous work has presented an architecture for a collaboration online application which allows users in different locations to share videos, images and documents while at the same time video chatting. The application's servers are deployed in a cloud environment which can scale up and down based on demand. Furthermore, the design allows the application to be deployed on multiple clouds which are deployed in different geographic locations. Previous work however did not introduce how the application's up and down scaling is to be achieved. In this paper the autonomic system which manages the self-optimizing function of the cloud is presented. The autonomic system itself is a self-organizing system with a control model based on the leaky-bucket theory often used in network congestion control. A testbed for the collaboration application is used in order to gather performance metrics for the model.
{"title":"Self-optimizing autonomic control of geographically distributed collaboration applications","authors":"B. Solomon, D. Ionescu, C. Gadea, S. Veres, Marin Litoiu","doi":"10.1145/2494621.2494650","DOIUrl":"https://doi.org/10.1145/2494621.2494650","url":null,"abstract":"In the past few years, cloud computing has become an integral technology both for the day to day running of corporations, as well as in everyday life as more services are offered which use a backend cloud. At the same time online collaboration tools are becoming more important as both businesses and individuals need to share information and collaborate with other entities. Previous work has presented an architecture for a collaboration online application which allows users in different locations to share videos, images and documents while at the same time video chatting. The application's servers are deployed in a cloud environment which can scale up and down based on demand. Furthermore, the design allows the application to be deployed on multiple clouds which are deployed in different geographic locations. Previous work however did not introduce how the application's up and down scaling is to be achieved. In this paper the autonomic system which manages the self-optimizing function of the cloud is presented. The autonomic system itself is a self-organizing system with a control model based on the leaky-bucket theory often used in network congestion control. A testbed for the collaboration application is used in order to gather performance metrics for the model.","PeriodicalId":190559,"journal":{"name":"ACM Cloud and Autonomic Computing Conference","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134116409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We present a federation model to support the dynamic federation of resources and autonomic management mechanisms that coordinate multiple workflows to use resources based on objectives. We illustrate the effectiveness of the proposed framework and autonomic mechanisms through the discussion of representative use case application scenarios, and from these experiences, we discuss that such a federation model can support new types of application formulations.
{"title":"Enabling autonomic computing on federated advanced cyberinfrastructures","authors":"J. Montes, Mengsong Zou, I. Rodero, M. Parashar","doi":"10.1145/2494621.2494641","DOIUrl":"https://doi.org/10.1145/2494621.2494641","url":null,"abstract":"We present a federation model to support the dynamic federation of resources and autonomic management mechanisms that coordinate multiple workflows to use resources based on objectives. We illustrate the effectiveness of the proposed framework and autonomic mechanisms through the discussion of representative use case application scenarios, and from these experiences, we discuss that such a federation model can support new types of application formulations.","PeriodicalId":190559,"journal":{"name":"ACM Cloud and Autonomic Computing Conference","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121412535","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}
To support the variable load of Massively Multiplayer On-line Games (MMOGs) with millions of registered users and thousands of active concurrent players, game operators over-provision a large static infrastructure capable of sustaining the peak load with guaranteed Quality of Service (QoS). This leads to inefficient resource utilisation, high service prices, and limited market participation accessible only to the large companies. To address this problem, we propose a new autonomic ecosystem for hosting and operating MMOGs based on cloud computing principles involving four smaller and better focused business actors whose interaction is regulated through Service Level Agreements (SLAs): resource provider, game operator, game provider, and client. In our model, game providers acquire operation SLAs from game operators to satisfy client requests and manage multiple distributed MMOG sessions. Game operators lease on-demand cloud resources based on the dynamic MMOG load and guarantee the required QoS to all clients. We evaluate through simulations based on real MMOG traces and commercial cloud SLAs different methods of ranking MMOG operation offers. We show that considering compensations for SLA faults in the offer selection can lead to over 11% gains in game providers' income, and that adequate ranking of offers can reduce operational costs by up to 60%.
{"title":"Autonomic operation of massively multiplayer online games in clouds","authors":"Vlad Nae, R. Prodan, A. Iosup","doi":"10.1145/2494621.2494629","DOIUrl":"https://doi.org/10.1145/2494621.2494629","url":null,"abstract":"To support the variable load of Massively Multiplayer On-line Games (MMOGs) with millions of registered users and thousands of active concurrent players, game operators over-provision a large static infrastructure capable of sustaining the peak load with guaranteed Quality of Service (QoS). This leads to inefficient resource utilisation, high service prices, and limited market participation accessible only to the large companies. To address this problem, we propose a new autonomic ecosystem for hosting and operating MMOGs based on cloud computing principles involving four smaller and better focused business actors whose interaction is regulated through Service Level Agreements (SLAs): resource provider, game operator, game provider, and client. In our model, game providers acquire operation SLAs from game operators to satisfy client requests and manage multiple distributed MMOG sessions. Game operators lease on-demand cloud resources based on the dynamic MMOG load and guarantee the required QoS to all clients. We evaluate through simulations based on real MMOG traces and commercial cloud SLAs different methods of ranking MMOG operation offers. We show that considering compensations for SLA faults in the offer selection can lead to over 11% gains in game providers' income, and that adequate ranking of offers can reduce operational costs by up to 60%.","PeriodicalId":190559,"journal":{"name":"ACM Cloud and Autonomic Computing Conference","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134416128","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}
Seoyoung Kim, Jik-Soo Kim, Soonwook Hwang, Yoonhee Kim
Recent cloud computing enables numerous scientists to earn advantages by serving on-demand and elastic resources whenever they desire computing resources. This science cloud paradigm has been actively developed and investigated to satisfy requirements of the scientists such as performance, feasibility and so on. However, effective allocation and provisioning virtual machines on clouds are still considered as a challenging issue in scientists using high throughput computing, since it determines whether they can earn benefits from economy of scale in clouds or not. Moreover, allocating the "right" provisioned cloud resources on an optimal data center is very important as performance can vary widely depending on where and under what circumstances it actually runs. In these reasons, it is required that an appropriate and suitable model for science cloud to support increasing scientists and computations. In this paper, we present an allocation and provisioning model of science cloud, especially for high throughput computing applications. In this model, we utilize job traces where statistical method is applied to pick the most influential features for improving application performance. With the feature, the system determines where VM is deployed (allocation) and which instance type is proper (provisioning). An adaptive evaluation step which is subsequent to the job execution enables our model to adapt to dynamical computing environments. We show performance achievements as comparing the proposed model with other policies through experiments. Finally, we expect that improvement on performance as well as reduction of cost from resource consumption through our model.
{"title":"An allocation and provisioning model of science cloud for high throughput computing applications","authors":"Seoyoung Kim, Jik-Soo Kim, Soonwook Hwang, Yoonhee Kim","doi":"10.1145/2494621.2494649","DOIUrl":"https://doi.org/10.1145/2494621.2494649","url":null,"abstract":"Recent cloud computing enables numerous scientists to earn advantages by serving on-demand and elastic resources whenever they desire computing resources. This science cloud paradigm has been actively developed and investigated to satisfy requirements of the scientists such as performance, feasibility and so on. However, effective allocation and provisioning virtual machines on clouds are still considered as a challenging issue in scientists using high throughput computing, since it determines whether they can earn benefits from economy of scale in clouds or not. Moreover, allocating the \"right\" provisioned cloud resources on an optimal data center is very important as performance can vary widely depending on where and under what circumstances it actually runs. In these reasons, it is required that an appropriate and suitable model for science cloud to support increasing scientists and computations.\u0000 In this paper, we present an allocation and provisioning model of science cloud, especially for high throughput computing applications. In this model, we utilize job traces where statistical method is applied to pick the most influential features for improving application performance. With the feature, the system determines where VM is deployed (allocation) and which instance type is proper (provisioning). An adaptive evaluation step which is subsequent to the job execution enables our model to adapt to dynamical computing environments. We show performance achievements as comparing the proposed model with other policies through experiments. Finally, we expect that improvement on performance as well as reduction of cost from resource consumption through our model.","PeriodicalId":190559,"journal":{"name":"ACM Cloud and Autonomic Computing Conference","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125414129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The increasing spread of elastic Cloud services, together with the pay-as-you-go pricing model of Cloud computing, has led to the need of an elasticity controller. The controller automatically resizes an elastic service in response to changes in workload, in order to meet Service Level Objectives (SLOs) at a reduced cost. However, variable performance of Cloud Virtual Machines and nonlinearities in Cloud services, such as the diminishing reward of adding a service instance with increasing the scale, complicates the controller design. We present the design and evaluation of ElastMan, an elasticity controller for Cloud-based elastic key-value stores. ElastMan combines feedforward and feedback control. Feedforward control is used to respond to spikes in the workload by quickly resizing the service to meet SLOs at a minimal cost. Feedback control is used to correct modeling errors and to handle diurnal workload. To address nonlinearities, our design of ElastMan leverages the near-linear scalability of elastic Cloud services in order to build a scale-independent model of the service. We have implemented and evaluated ElastMan using the Voldemort key-value store running in an OpenStack Cloud environment. Our evaluation shows the feasibility and effectiveness of our approach to automation of Cloud service elasticity.
{"title":"ElastMan: elasticity manager for elastic key-value stores in the cloud","authors":"A. Al-Shishtawy, Vladimir Vlassov","doi":"10.1145/2494621.2494630","DOIUrl":"https://doi.org/10.1145/2494621.2494630","url":null,"abstract":"The increasing spread of elastic Cloud services, together with the pay-as-you-go pricing model of Cloud computing, has led to the need of an elasticity controller. The controller automatically resizes an elastic service in response to changes in workload, in order to meet Service Level Objectives (SLOs) at a reduced cost. However, variable performance of Cloud Virtual Machines and nonlinearities in Cloud services, such as the diminishing reward of adding a service instance with increasing the scale, complicates the controller design. We present the design and evaluation of ElastMan, an elasticity controller for Cloud-based elastic key-value stores. ElastMan combines feedforward and feedback control. Feedforward control is used to respond to spikes in the workload by quickly resizing the service to meet SLOs at a minimal cost. Feedback control is used to correct modeling errors and to handle diurnal workload. To address nonlinearities, our design of ElastMan leverages the near-linear scalability of elastic Cloud services in order to build a scale-independent model of the service. We have implemented and evaluated ElastMan using the Voldemort key-value store running in an OpenStack Cloud environment. Our evaluation shows the feasibility and effectiveness of our approach to automation of Cloud service elasticity.","PeriodicalId":190559,"journal":{"name":"ACM Cloud and Autonomic Computing Conference","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132744486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The ubiquity of database systems and the emergence of new and different threats require multiple and overlapping security mechanisms. Providing multiple and diverse database intrusion detection and prevention systems (IDPS) is a critical component of the defense-in-depth strategy for DB information systems. However, providing this level of security can greatly impact a system's QoS requirements. It would then be advantageous to use the combination of IDPSs that best meets the security and QoS concerns of the system stakeholders for each workload intensity level. Due to the dynamic variability of the workload intensity, it is not feasible for human beings to continuously reconfigure the system. We offer an autonomic computing approach for a self-protecting and self-optimizing database system environment that captures dynamic and fine-grained tradeoffs between security and QoS. The approach uses a multi-objective utility function that considers security overhead, perceived risk level, and high level stakeholder objectives. We describe the implementation of an autonomic controller that uses combinatorial search techniques and queuing network models to dynamically search for a near-optimal security configuration. We validate our approach experimentally on a TPC-W e-commerce site and show that our approach balances QoS and security goals.
{"title":"Self-protecting and self-optimizing database systems: implementation and experimental evaluation","authors":"Firas B. Alomari, D. Menascé","doi":"10.1145/2494621.2494631","DOIUrl":"https://doi.org/10.1145/2494621.2494631","url":null,"abstract":"The ubiquity of database systems and the emergence of new and different threats require multiple and overlapping security mechanisms. Providing multiple and diverse database intrusion detection and prevention systems (IDPS) is a critical component of the defense-in-depth strategy for DB information systems. However, providing this level of security can greatly impact a system's QoS requirements. It would then be advantageous to use the combination of IDPSs that best meets the security and QoS concerns of the system stakeholders for each workload intensity level. Due to the dynamic variability of the workload intensity, it is not feasible for human beings to continuously reconfigure the system. We offer an autonomic computing approach for a self-protecting and self-optimizing database system environment that captures dynamic and fine-grained tradeoffs between security and QoS. The approach uses a multi-objective utility function that considers security overhead, perceived risk level, and high level stakeholder objectives. We describe the implementation of an autonomic controller that uses combinatorial search techniques and queuing network models to dynamically search for a near-optimal security configuration. We validate our approach experimentally on a TPC-W e-commerce site and show that our approach balances QoS and security goals.","PeriodicalId":190559,"journal":{"name":"ACM Cloud and Autonomic Computing Conference","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133271041","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}
Despite the potential given by the combination of multi-tenancy and virtualization, resource utilization in today's data centers is still low. We identify three key characteristics of cloud services and infrastructure as-a-service management practices: burstiness in service workloads, fluctuations in virtual machine resource usage over time, and virtual machines being limited to pre-defined sizes only. Based on these characteristics, we propose scheduling and admission control algorithms that incorporate resource overbooking to improve utilization. A combination of modeling, monitoring, and prediction techniques is used to avoid overpassing the total infrastructure capacity. A performance evaluation using a mixture of workload traces demonstrates the potential for significant improvements in resource utilization while still avoiding overpassing the total capacity.
{"title":"Improving cloud infrastructure utilization through overbooking","authors":"Luis Tomás, Johan Tordsson","doi":"10.1145/2494621.2494627","DOIUrl":"https://doi.org/10.1145/2494621.2494627","url":null,"abstract":"Despite the potential given by the combination of multi-tenancy and virtualization, resource utilization in today's data centers is still low. We identify three key characteristics of cloud services and infrastructure as-a-service management practices: burstiness in service workloads, fluctuations in virtual machine resource usage over time, and virtual machines being limited to pre-defined sizes only. Based on these characteristics, we propose scheduling and admission control algorithms that incorporate resource overbooking to improve utilization. A combination of modeling, monitoring, and prediction techniques is used to avoid overpassing the total infrastructure capacity. A performance evaluation using a mixture of workload traces demonstrates the potential for significant improvements in resource utilization while still avoiding overpassing the total capacity.","PeriodicalId":190559,"journal":{"name":"ACM Cloud and Autonomic Computing Conference","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127643456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The elasticity afforded by cloud computing allows consumers to dynamically request and relinquish computing and storage resources and pay for them on a pay-per-use basis. Cloud computing providers rely on virtualization techniques to manage the dynamic nature of their infrastructure allowing consumers to dynamically allocate and deallocate virtual machines of different capacities. Cloud providers need to optimally decide the best allocation of virtual machines to physical machines as the demand varies dynamically. When making such decisions, cloud providers can migrate VMs already allocated and/or use external cloud providers. This paper considers the problem in which the cloud provider wants to maximize its revenue, subject to capacity, availability SLA, and VM migration constraints. The paper presents a heuristic solution, called Near Optimal (NOPT), to this NP-hard problem and discusses the results of its experimental evaluation in comparison with a best fit (BF) allocation strategy. The results show that NOPT provides a 45% improvement in average revenue when compared with BF for the parameters used in the experiment. Moreover, the NOPT algorithm maintained the availability close to one for all classes of users while BF exhibited a lower availability and even failed to meet the availability SLA at times.
{"title":"Autonomic resource provisioning in cloud systems with availability goals","authors":"E. Casalicchio, D. Menascé, Arwa Aldhalaan","doi":"10.1145/2494621.2494623","DOIUrl":"https://doi.org/10.1145/2494621.2494623","url":null,"abstract":"The elasticity afforded by cloud computing allows consumers to dynamically request and relinquish computing and storage resources and pay for them on a pay-per-use basis. Cloud computing providers rely on virtualization techniques to manage the dynamic nature of their infrastructure allowing consumers to dynamically allocate and deallocate virtual machines of different capacities. Cloud providers need to optimally decide the best allocation of virtual machines to physical machines as the demand varies dynamically. When making such decisions, cloud providers can migrate VMs already allocated and/or use external cloud providers. This paper considers the problem in which the cloud provider wants to maximize its revenue, subject to capacity, availability SLA, and VM migration constraints. The paper presents a heuristic solution, called Near Optimal (NOPT), to this NP-hard problem and discusses the results of its experimental evaluation in comparison with a best fit (BF) allocation strategy. The results show that NOPT provides a 45% improvement in average revenue when compared with BF for the parameters used in the experiment. Moreover, the NOPT algorithm maintained the availability close to one for all classes of users while BF exhibited a lower availability and even failed to meet the availability SLA at times.","PeriodicalId":190559,"journal":{"name":"ACM Cloud and Autonomic Computing Conference","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116925027","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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