The Experts below are selected from a list of 961926 Experts worldwide ranked by ideXlab platform
Alaa S Youssef - One of the best experts on this subject based on the ideXlab platform.
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Performance Management for cluster based web services
2005Co-Authors: Giovanni Pacifici, Asser N Tantawi, Mike Spreitzer, Alaa S YoussefAbstract:We present an architecture and prototype implementation of a Performance Management system for cluster-based web services. The system supports multiple classes of web services traffic and allocates server resources dynamically so to maximize the expected value of a given cluster utility function in the face of fluctuating loads. The cluster utility is a function of the Performance delivered to the various classes, and this leads to differentiated service. In this paper, we will use the average response time as the Performance metric. The Management system is transparent: it requires no changes in the client code, the server code, or the network interface between them. The system performs three Performance Management tasks: resource allocation, load balancing, and server overload protection. We use two nested levels of Management. The inner level centers on queuing and scheduling of request messages. The outer level is a feedback control loop that periodically adjusts the scheduling weights and server allocations of the inner level. The feedback controller is based on an approximate first-principles model of the system, with parameters derived from continuous monitoring. We focus on SOAP-based web services. We report experimental results that show the dynamic behavior of the system.
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Performance Management for cluster based web services
2003Co-Authors: R Levy, J Nagarajarao, Giovanni Pacifici, A Spreitzer, Asser N Tantawi, Alaa S YoussefAbstract:We present an architecture and prototype implementation of a Performance Management system for cluster-based Web services. The system supports multiple classes of Web services traffic and allocates server resources dynamically so to maximize the expected value of a given cluster utility function in the face of fluctuating loads. The cluster utility is a function of the Performance delivered to the various classes, and this leads to Differentiated Service. In this paper we use the average response time as the Performance metric. The Management system is transparent: it requires no changes in the client code, the server code, or the network interface between them. The system performs three Performance Management tasks: resource allocation, load balancing, and server overload protection. We use two nested levels of Management mechanism. The inner level centers on queuing and scheduling of request messages. The outer level is a feedback control loop that periodically adjusts the scheduling weights and server allocations of the inner level. The feedback controller is based on an approximate first-principles model of the system, with parameters derived from continuous monitoring. We focus on SOAP-based Web services. We report experimental results that show the dynamic behavior of the system.
Mazin Yousif - One of the best experts on this subject based on the ideXlab platform.
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autonomic power and Performance Management for computing systems
2008Co-Authors: Bithika Khargharia, Salim Hariri, Mazin YousifAbstract:With the increased complexity of platforms, the growing demand of applications and data centers' servers sprawl, power consumption is reaching unsustainable limits. The need to improved power Management is becoming essential for many reasons including reduced power consumption & cooling, improved density, reliability & compliance with environmental standards. This paper presents a theoretical framework and methodology for autonomic power and Performance Management in e-business data centers. We optimize for power and Performance (Performance-per-watt) at each level of the hierarchy while maintaining scalability. We adopt mathematically-rigorous optimization approach to minimize power while meeting Performance constraints. Our experimental results show around 72% savings in power while maintaining Performance as compared to static power Management techniques and 69.8% additional savings with both global and local optimizations.
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autonomic power and Performance Management for computing systems
2006Co-Authors: Bithika Khargharia, Salim Hariri, Mazin YousifAbstract:With the increased complexity of platforms, the growing demand of applications and data centers' servers sprawl, power consumption is reaching unsustainable limits. The need to improved power Management is becoming essential for many reasons including reduced power consumption & cooling, improved density, reliability & compliance with environmental standards. This paper presents a theoretical framework and methodology for autonomic power and Performance Management in e-business data centers. We optimize for power and Performance (Performance/watt) at each level of the hierarchy while maintaining scalability. We adopt mathematically-rigorous optimization approach minimizing power while meeting Performance constraints. Our experimental results show around 72% savings in power as compared to static power Management techniques and 69.8% additional savings with both global and local optimizations.
Giovanni Pacifici - One of the best experts on this subject based on the ideXlab platform.
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Performance Management for cluster based web services
2005Co-Authors: Giovanni Pacifici, Asser N Tantawi, Mike Spreitzer, Alaa S YoussefAbstract:We present an architecture and prototype implementation of a Performance Management system for cluster-based web services. The system supports multiple classes of web services traffic and allocates server resources dynamically so to maximize the expected value of a given cluster utility function in the face of fluctuating loads. The cluster utility is a function of the Performance delivered to the various classes, and this leads to differentiated service. In this paper, we will use the average response time as the Performance metric. The Management system is transparent: it requires no changes in the client code, the server code, or the network interface between them. The system performs three Performance Management tasks: resource allocation, load balancing, and server overload protection. We use two nested levels of Management. The inner level centers on queuing and scheduling of request messages. The outer level is a feedback control loop that periodically adjusts the scheduling weights and server allocations of the inner level. The feedback controller is based on an approximate first-principles model of the system, with parameters derived from continuous monitoring. We focus on SOAP-based web services. We report experimental results that show the dynamic behavior of the system.
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Performance Management for cluster based web services
2003Co-Authors: R Levy, J Nagarajarao, Giovanni Pacifici, A Spreitzer, Asser N Tantawi, Alaa S YoussefAbstract:We present an architecture and prototype implementation of a Performance Management system for cluster-based Web services. The system supports multiple classes of Web services traffic and allocates server resources dynamically so to maximize the expected value of a given cluster utility function in the face of fluctuating loads. The cluster utility is a function of the Performance delivered to the various classes, and this leads to Differentiated Service. In this paper we use the average response time as the Performance metric. The Management system is transparent: it requires no changes in the client code, the server code, or the network interface between them. The system performs three Performance Management tasks: resource allocation, load balancing, and server overload protection. We use two nested levels of Management mechanism. The inner level centers on queuing and scheduling of request messages. The outer level is a feedback control loop that periodically adjusts the scheduling weights and server allocations of the inner level. The feedback controller is based on an approximate first-principles model of the system, with parameters derived from continuous monitoring. We focus on SOAP-based Web services. We report experimental results that show the dynamic behavior of the system.
Asser N Tantawi - One of the best experts on this subject based on the ideXlab platform.
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Performance Management for cluster based web services
2005Co-Authors: Giovanni Pacifici, Asser N Tantawi, Mike Spreitzer, Alaa S YoussefAbstract:We present an architecture and prototype implementation of a Performance Management system for cluster-based web services. The system supports multiple classes of web services traffic and allocates server resources dynamically so to maximize the expected value of a given cluster utility function in the face of fluctuating loads. The cluster utility is a function of the Performance delivered to the various classes, and this leads to differentiated service. In this paper, we will use the average response time as the Performance metric. The Management system is transparent: it requires no changes in the client code, the server code, or the network interface between them. The system performs three Performance Management tasks: resource allocation, load balancing, and server overload protection. We use two nested levels of Management. The inner level centers on queuing and scheduling of request messages. The outer level is a feedback control loop that periodically adjusts the scheduling weights and server allocations of the inner level. The feedback controller is based on an approximate first-principles model of the system, with parameters derived from continuous monitoring. We focus on SOAP-based web services. We report experimental results that show the dynamic behavior of the system.
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Performance Management for cluster based web services
2003Co-Authors: R Levy, J Nagarajarao, Giovanni Pacifici, A Spreitzer, Asser N Tantawi, Alaa S YoussefAbstract:We present an architecture and prototype implementation of a Performance Management system for cluster-based Web services. The system supports multiple classes of Web services traffic and allocates server resources dynamically so to maximize the expected value of a given cluster utility function in the face of fluctuating loads. The cluster utility is a function of the Performance delivered to the various classes, and this leads to Differentiated Service. In this paper we use the average response time as the Performance metric. The Management system is transparent: it requires no changes in the client code, the server code, or the network interface between them. The system performs three Performance Management tasks: resource allocation, load balancing, and server overload protection. We use two nested levels of Management mechanism. The inner level centers on queuing and scheduling of request messages. The outer level is a feedback control loop that periodically adjusts the scheduling weights and server allocations of the inner level. The feedback controller is based on an approximate first-principles model of the system, with parameters derived from continuous monitoring. We focus on SOAP-based Web services. We report experimental results that show the dynamic behavior of the system.
Bithika Khargharia - One of the best experts on this subject based on the ideXlab platform.
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autonomic power and Performance Management for computing systems
2008Co-Authors: Bithika Khargharia, Salim Hariri, Mazin YousifAbstract:With the increased complexity of platforms, the growing demand of applications and data centers' servers sprawl, power consumption is reaching unsustainable limits. The need to improved power Management is becoming essential for many reasons including reduced power consumption & cooling, improved density, reliability & compliance with environmental standards. This paper presents a theoretical framework and methodology for autonomic power and Performance Management in e-business data centers. We optimize for power and Performance (Performance-per-watt) at each level of the hierarchy while maintaining scalability. We adopt mathematically-rigorous optimization approach to minimize power while meeting Performance constraints. Our experimental results show around 72% savings in power while maintaining Performance as compared to static power Management techniques and 69.8% additional savings with both global and local optimizations.
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autonomic power and Performance Management for computing systems
2006Co-Authors: Bithika Khargharia, Salim Hariri, Mazin YousifAbstract:With the increased complexity of platforms, the growing demand of applications and data centers' servers sprawl, power consumption is reaching unsustainable limits. The need to improved power Management is becoming essential for many reasons including reduced power consumption & cooling, improved density, reliability & compliance with environmental standards. This paper presents a theoretical framework and methodology for autonomic power and Performance Management in e-business data centers. We optimize for power and Performance (Performance/watt) at each level of the hierarchy while maintaining scalability. We adopt mathematically-rigorous optimization approach minimizing power while meeting Performance constraints. Our experimental results show around 72% savings in power as compared to static power Management techniques and 69.8% additional savings with both global and local optimizations.
