The Experts below are selected from a list of 261 Experts worldwide ranked by ideXlab platform
Caio Pereira Oliveira - One of the best experts on this subject based on the ideXlab platform.
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Koordinator: A Service Approach for Replicating Docker Containers in Kubernetes
2018 IEEE Symposium on Computers and Communications (ISCC), 2018Co-Authors: Hylson Vescovi Netto, Aldelir Fernando Luiz, Miguel Correia, Luciana De Oliveira Rech, Caio Pereira OliveiraAbstract:Container-based Virtualization technologies such as Docker and Kubernetes are being adopted by cloud service providers due to their simpler deployment, better performance, and lower memory footprint in relation to Hypervisor-Based Virtualization. Kubernetes supports basic replication for availability, but does not provide strong consistency and may corrupt application state in case there is a fault. This paper presents a state machine replication scheme for Kubernetes that provides high availability and integrity with strong consistency. Replica coordination is provided as a service, with lightweight coupling to applications. Experimental results show the solution feasibility.
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ISCC - Koordinator: A Service Approach for Replicating Docker Containers in Kubernetes
2018 IEEE Symposium on Computers and Communications (ISCC), 2018Co-Authors: Hylson Vescovi Netto, Aldelir Fernando Luiz, Miguel Correia, Luciana Rech, Caio Pereira OliveiraAbstract:Container-based Virtualization technologies such as Docker and Kubernetes are being adopted by cloud service providers due to their simpler deployment, better performance, and lower memory footprint in relation to Hypervisor-Based Virtualization. Kubernetes supports basic replication for availability, but does not provide strong consistency and may corrupt application state in case there is a fault. This paper presents a state machine replication scheme for Kubernetes that provides high availability and integrity with strong consistency. Replica coordination is provided as a service, with lightweight coupling to applications. Experimental results show the solution feasibility.
Ajay Gulati - One of the best experts on this subject based on the ideXlab platform.
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Chethan Kumar
2011Co-Authors: Ajay Gulati, Vmware Inc, Karan Kumar, Irfan AhmadAbstract:Live migration of virtual hard disks between storage arrays has long been possible. However, there is a dearth of online tools to perform automated virtual disk placement and IO load balancing across multiple storage arrays. This problem is quite challenging because the performance of IO workloads depends heavily on their own characteristics and that of the underlying storage device. Moreover, many device-specific details are hidden behind the interface exposed by storage arrays. In this paper, we introduce BASIL, a novel software system that automatically manages virtual disk placement and performs load balancing across devices without assuming any support from the storage arrays. BASIL uses IO latency as a primary metric for modeling. Our technique involves separate online modeling of workloads and storage devices. BASIL uses these models to recommend migrations between devices to balance load and improve overall performance. We present the design and implementation of BASIL in the context of VMware ESX, a Hypervisor-Based Virtualization system, and demonstrate that the modeling works well for a wide range of workloads and devices. We evaluate the placements recommended by BASIL, and show that they lead to improvements of at least 25 % in both latency and throughput for 80 percent of the hundreds of microbenchmark configurations we ran. When tested with enterprise applications, BASIL performed favorably versus human experts, improving latency by 18-27%.
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basil automated io load balancing across storage devices
File and Storage Technologies, 2010Co-Authors: Ajay Gulati, Irfan Ahmad, Chethan Kumar, Karan KumarAbstract:Live migration of virtual hard disks between storage arrays has long been possible. However, there is a dearth of online tools to perform automated virtual disk placement and IO load balancing across multiple storage arrays. This problem is quite challenging because the performance of IO workloads depends heavily on their own characteristics and that of the underlying storage device. Moreover, many device-specific details are hidden behind the interface exposed by storage arrays.In this paper, we introduce BASIL, a novel software system that automatically manages virtual disk placement and performs load balancing across devices without assuming any support from the storage arrays. BASIL uses IO latency as a primary metric for modeling. Our technique involves separate online modeling of workloads and storage devices. BASIL uses these models to recommend migrations between devices to balance load and improve overall performance.We present the design and implementation of BASIL in the context of VMware ESX, a Hypervisor-Based Virtualization system, and demonstrate that the modeling works well for a wide range of workloads and devices. We evaluate the placements recommended by BASIL, and show that they lead to improvements of at least 25% in both latency and throughput for 80 percent of the hundreds of microbenchmark configurations we ran. When tested with enterprise applications, BASIL performed favorably versus human experts, improving latency by 18-27%.
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FAST - BASIL: automated IO load balancing across storage devices
2010Co-Authors: Ajay Gulati, Irfan Ahmad, Chethan Kumar, Karan KumarAbstract:Live migration of virtual hard disks between storage arrays has long been possible. However, there is a dearth of online tools to perform automated virtual disk placement and IO load balancing across multiple storage arrays. This problem is quite challenging because the performance of IO workloads depends heavily on their own characteristics and that of the underlying storage device. Moreover, many device-specific details are hidden behind the interface exposed by storage arrays. In this paper, we introduce BASIL, a novel software system that automatically manages virtual disk placement and performs load balancing across devices without assuming any support from the storage arrays. BASIL uses IO latency as a primary metric for modeling. Our technique involves separate online modeling of workloads and storage devices. BASIL uses these models to recommend migrations between devices to balance load and improve overall performance. We present the design and implementation of BASIL in the context of VMware ESX, a Hypervisor-Based Virtualization system, and demonstrate that the modeling works well for a wide range of workloads and devices. We evaluate the placements recommended by BASIL, and show that they lead to improvements of at least 25% in both latency and throughput for 80 percent of the hundreds of microbenchmark configurations we ran. When tested with enterprise applications, BASIL performed favorably versus human experts, improving latency by 18-27%.
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parda proportional allocation of resources for distributed storage access
File and Storage Technologies, 2009Co-Authors: Ajay Gulati, Irfan Ahmad, Carl A WaldspurgerAbstract:Rapid adoption of Virtualization technologies has led to increased utilization of physical resources, which are multiplexed among numerous workloads with varying demands and importance. Virtualization has also accelerated the deployment of shared storage systems, which offer many advantages in such environments. Effective resource management for shared storage systems is challenging, even in research systems with complete end-to-end control over all system components. Commercially-available storage arrays typically offer only limited, proprietary support for controlling service rates, which is insufficient for isolating workloads sharing the same storage volume or LUN. To address these issues, we introduce PARDA, a novel software system that enforces proportional-share fairness among distributed hosts accessing a storage array, without assuming any support from the array itself. PARDA uses latency measurements to detect overload, and adjusts issue queue lengths to provide fairness, similar to aspects of flow control in FAST TCP. We present the design and implementation of PARDA in the context of VMware ESX Server, a Hypervisor-Based Virtualization system, and show how it can be used to provide differential quality of service for unmodified virtual machines while maintaining high efficiency. We evaluate the effectiveness of our implementation using quantitative experiments, demonstrating that this approach is practical.
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FAST - PARDA: proportional allocation of resources for distributed storage access
2009Co-Authors: Ajay Gulati, Irfan Ahmad, Carl A WaldspurgerAbstract:Rapid adoption of Virtualization technologies has led to increased utilization of physical resources, which are multiplexed among numerous workloads with varying demands and importance. Virtualization has also accelerated the deployment of shared storage systems, which offer many advantages in such environments. Effective resource management for shared storage systems is challenging, even in research systems with complete end-to-end control over all system components. Commercially-available storage arrays typically offer only limited, proprietary support for controlling service rates, which is insufficient for isolating workloads sharing the same storage volume or LUN. To address these issues, we introduce PARDA, a novel software system that enforces proportional-share fairness among distributed hosts accessing a storage array, without assuming any support from the array itself. PARDA uses latency measurements to detect overload, and adjusts issue queue lengths to provide fairness, similar to aspects of flow control in FAST TCP. We present the design and implementation of PARDA in the context of VMware ESX Server, a Hypervisor-Based Virtualization system, and show how it can be used to provide differential quality of service for unmodified virtual machines while maintaining high efficiency. We evaluate the effectiveness of our implementation using quantitative experiments, demonstrating that this approach is practical.
Roberto Morabito - One of the best experts on this subject based on the ideXlab platform.
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UCC - Power consumption of visualization technologies: an empirical investigation
2015Co-Authors: Roberto MorabitoAbstract:Virtualization is growing rapidly as a result of the increasing number of alternative solutions in this area, and of the wide range of application field. Until now, Hypervisor-Based Virtualization has been the de facto solution to perform server Virtualization. Recently, container-based Virtualization - an alternative to hypervisors - has gained more attention because of lightweight characteristics, attracting cloud providers that have already made use of it to deliver their services. However, a gap in the existing research on containers exists in the area of power consumption. This paper presents the results of a performance comparison in terms of power consumption of four different Virtualization technologies: KVM and Xen, which are based on hypervisor Virtualization, Docker and LXC which are based on container Virtualization. The aim of this empirical investigation, carried out by means of a testbed, is to understand how these technologies react to particular workloads. Our initial results show how, despite of the number of virtual entities running, both kinds of Virtualization alternatives behave similarly in idle state and in CPU/Memory stress test. Contrarily, the results on network performance show differences between the two technologies.
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hypervisors vs lightweight Virtualization a performance comparison
IEEE International Conference on Cloud Engineering, 2015Co-Authors: Roberto Morabito, Jimmy Kjallman, Miika KomuAbstract:Virtualization of operating systems provides a common way to run different services in the cloud. Recently, the lightweight Virtualization technologies claim to offer superior performance. In this paper, we present a detailed performance comparison of traditional hypervisor based Virtualization and new lightweight solutions. In our measurements, we use several benchmarks tools in order to understand the strengths, weaknesses, and anomalies introduced by these different platforms in terms of processing, storage, memory and network. Our results show that containers achieve generally better performance when compared with traditional virtual machines and other recent solutions. Albeit containers offer clearly more dense deployment of virtual machines, the performance difference with other technologies is in many cases relatively small.
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IC2E - Hypervisors vs. Lightweight Virtualization: A Performance Comparison
2015 IEEE International Conference on Cloud Engineering, 2015Co-Authors: Roberto Morabito, Jimmy Kjallman, Miika KomuAbstract:Virtualization of operating systems provides a common way to run different services in the cloud. Recently, the lightweight Virtualization technologies claim to offer superior performance. In this paper, we present a detailed performance comparison of traditional hypervisor based Virtualization and new lightweight solutions. In our measurements, we use several benchmarks tools in order to understand the strengths, weaknesses, and anomalies introduced by these different platforms in terms of processing, storage, memory and network. Our results show that containers achieve generally better performance when compared with traditional virtual machines and other recent solutions. Albeit containers offer clearly more dense deployment of virtual machines, the performance difference with other technologies is in many cases relatively small.
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Power Consumption of Virtualization Technologies: An Empirical Investigation
2015 IEEE ACM 8th International Conference on Utility and Cloud Computing (UCC), 2015Co-Authors: Roberto MorabitoAbstract:Virtualization is growing rapidly as a result of the increasing number of alternative solutions in this area, and of the wide range of application field. Until now, Hypervisor-Based Virtualization has been the de facto solution to perform server Virtualization. Recently, container-based Virtualization -- an alternative to hypervisors -- has gained more attention because of lightweight characteristics, attracting cloud providers that have already made use of it to deliver their services. However, a gap in the existing research on containers exists in the area of power consumption. This paper presents the results of a performance comparison in terms of power consumption of four different Virtualization technologies: KVM and Xen, which are based on hypervisor Virtualization, Docker and LXC which are based on container Virtualization. The aim of this empirical investigation, carried out by means of a testbed, is to understand how these technologies react to particular workloads. Our initial results show how, despite of the number of virtual entities running, both kinds of Virtualization alternatives behave similarly in idle state and in CPU/Memory stress test. Contrarily, the results on network performance show differences between the two technologies.
Irfan Ahmad - One of the best experts on this subject based on the ideXlab platform.
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Chethan Kumar
2011Co-Authors: Ajay Gulati, Vmware Inc, Karan Kumar, Irfan AhmadAbstract:Live migration of virtual hard disks between storage arrays has long been possible. However, there is a dearth of online tools to perform automated virtual disk placement and IO load balancing across multiple storage arrays. This problem is quite challenging because the performance of IO workloads depends heavily on their own characteristics and that of the underlying storage device. Moreover, many device-specific details are hidden behind the interface exposed by storage arrays. In this paper, we introduce BASIL, a novel software system that automatically manages virtual disk placement and performs load balancing across devices without assuming any support from the storage arrays. BASIL uses IO latency as a primary metric for modeling. Our technique involves separate online modeling of workloads and storage devices. BASIL uses these models to recommend migrations between devices to balance load and improve overall performance. We present the design and implementation of BASIL in the context of VMware ESX, a Hypervisor-Based Virtualization system, and demonstrate that the modeling works well for a wide range of workloads and devices. We evaluate the placements recommended by BASIL, and show that they lead to improvements of at least 25 % in both latency and throughput for 80 percent of the hundreds of microbenchmark configurations we ran. When tested with enterprise applications, BASIL performed favorably versus human experts, improving latency by 18-27%.
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basil automated io load balancing across storage devices
File and Storage Technologies, 2010Co-Authors: Ajay Gulati, Irfan Ahmad, Chethan Kumar, Karan KumarAbstract:Live migration of virtual hard disks between storage arrays has long been possible. However, there is a dearth of online tools to perform automated virtual disk placement and IO load balancing across multiple storage arrays. This problem is quite challenging because the performance of IO workloads depends heavily on their own characteristics and that of the underlying storage device. Moreover, many device-specific details are hidden behind the interface exposed by storage arrays.In this paper, we introduce BASIL, a novel software system that automatically manages virtual disk placement and performs load balancing across devices without assuming any support from the storage arrays. BASIL uses IO latency as a primary metric for modeling. Our technique involves separate online modeling of workloads and storage devices. BASIL uses these models to recommend migrations between devices to balance load and improve overall performance.We present the design and implementation of BASIL in the context of VMware ESX, a Hypervisor-Based Virtualization system, and demonstrate that the modeling works well for a wide range of workloads and devices. We evaluate the placements recommended by BASIL, and show that they lead to improvements of at least 25% in both latency and throughput for 80 percent of the hundreds of microbenchmark configurations we ran. When tested with enterprise applications, BASIL performed favorably versus human experts, improving latency by 18-27%.
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FAST - BASIL: automated IO load balancing across storage devices
2010Co-Authors: Ajay Gulati, Irfan Ahmad, Chethan Kumar, Karan KumarAbstract:Live migration of virtual hard disks between storage arrays has long been possible. However, there is a dearth of online tools to perform automated virtual disk placement and IO load balancing across multiple storage arrays. This problem is quite challenging because the performance of IO workloads depends heavily on their own characteristics and that of the underlying storage device. Moreover, many device-specific details are hidden behind the interface exposed by storage arrays. In this paper, we introduce BASIL, a novel software system that automatically manages virtual disk placement and performs load balancing across devices without assuming any support from the storage arrays. BASIL uses IO latency as a primary metric for modeling. Our technique involves separate online modeling of workloads and storage devices. BASIL uses these models to recommend migrations between devices to balance load and improve overall performance. We present the design and implementation of BASIL in the context of VMware ESX, a Hypervisor-Based Virtualization system, and demonstrate that the modeling works well for a wide range of workloads and devices. We evaluate the placements recommended by BASIL, and show that they lead to improvements of at least 25% in both latency and throughput for 80 percent of the hundreds of microbenchmark configurations we ran. When tested with enterprise applications, BASIL performed favorably versus human experts, improving latency by 18-27%.
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parda proportional allocation of resources for distributed storage access
File and Storage Technologies, 2009Co-Authors: Ajay Gulati, Irfan Ahmad, Carl A WaldspurgerAbstract:Rapid adoption of Virtualization technologies has led to increased utilization of physical resources, which are multiplexed among numerous workloads with varying demands and importance. Virtualization has also accelerated the deployment of shared storage systems, which offer many advantages in such environments. Effective resource management for shared storage systems is challenging, even in research systems with complete end-to-end control over all system components. Commercially-available storage arrays typically offer only limited, proprietary support for controlling service rates, which is insufficient for isolating workloads sharing the same storage volume or LUN. To address these issues, we introduce PARDA, a novel software system that enforces proportional-share fairness among distributed hosts accessing a storage array, without assuming any support from the array itself. PARDA uses latency measurements to detect overload, and adjusts issue queue lengths to provide fairness, similar to aspects of flow control in FAST TCP. We present the design and implementation of PARDA in the context of VMware ESX Server, a Hypervisor-Based Virtualization system, and show how it can be used to provide differential quality of service for unmodified virtual machines while maintaining high efficiency. We evaluate the effectiveness of our implementation using quantitative experiments, demonstrating that this approach is practical.
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FAST - PARDA: proportional allocation of resources for distributed storage access
2009Co-Authors: Ajay Gulati, Irfan Ahmad, Carl A WaldspurgerAbstract:Rapid adoption of Virtualization technologies has led to increased utilization of physical resources, which are multiplexed among numerous workloads with varying demands and importance. Virtualization has also accelerated the deployment of shared storage systems, which offer many advantages in such environments. Effective resource management for shared storage systems is challenging, even in research systems with complete end-to-end control over all system components. Commercially-available storage arrays typically offer only limited, proprietary support for controlling service rates, which is insufficient for isolating workloads sharing the same storage volume or LUN. To address these issues, we introduce PARDA, a novel software system that enforces proportional-share fairness among distributed hosts accessing a storage array, without assuming any support from the array itself. PARDA uses latency measurements to detect overload, and adjusts issue queue lengths to provide fairness, similar to aspects of flow control in FAST TCP. We present the design and implementation of PARDA in the context of VMware ESX Server, a Hypervisor-Based Virtualization system, and show how it can be used to provide differential quality of service for unmodified virtual machines while maintaining high efficiency. We evaluate the effectiveness of our implementation using quantitative experiments, demonstrating that this approach is practical.
Hylson Vescovi Netto - One of the best experts on this subject based on the ideXlab platform.
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Koordinator: A Service Approach for Replicating Docker Containers in Kubernetes
2018 IEEE Symposium on Computers and Communications (ISCC), 2018Co-Authors: Hylson Vescovi Netto, Aldelir Fernando Luiz, Miguel Correia, Luciana De Oliveira Rech, Caio Pereira OliveiraAbstract:Container-based Virtualization technologies such as Docker and Kubernetes are being adopted by cloud service providers due to their simpler deployment, better performance, and lower memory footprint in relation to Hypervisor-Based Virtualization. Kubernetes supports basic replication for availability, but does not provide strong consistency and may corrupt application state in case there is a fault. This paper presents a state machine replication scheme for Kubernetes that provides high availability and integrity with strong consistency. Replica coordination is provided as a service, with lightweight coupling to applications. Experimental results show the solution feasibility.
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ISCC - Koordinator: A Service Approach for Replicating Docker Containers in Kubernetes
2018 IEEE Symposium on Computers and Communications (ISCC), 2018Co-Authors: Hylson Vescovi Netto, Aldelir Fernando Luiz, Miguel Correia, Luciana Rech, Caio Pereira OliveiraAbstract:Container-based Virtualization technologies such as Docker and Kubernetes are being adopted by cloud service providers due to their simpler deployment, better performance, and lower memory footprint in relation to Hypervisor-Based Virtualization. Kubernetes supports basic replication for availability, but does not provide strong consistency and may corrupt application state in case there is a fault. This paper presents a state machine replication scheme for Kubernetes that provides high availability and integrity with strong consistency. Replica coordination is provided as a service, with lightweight coupling to applications. Experimental results show the solution feasibility.