Two Analytical Models (with Infinite Buffer) for Evaluating Performance of Gigabit Ethernet Hosts

Two analytical models are developed to study the impact of interrupt overhead on operating system performance of network hosts when subjected to Gigabit network traffic. Under heavy network traffic, the system performance will be negatively affected due to interrupt overhead caused by incoming traffic. In particular, excessive latency and significant degradation in system throughput can be experienced. Also, user applications may livelock as the CPU power is mostly consumed by interrupt handling and protocol processing. In this paper, we present and compare two analytical models that capture host behavior and evaluate its performance. The first model is based on Markov processes and queueing theory, while the second, which is more accurate but more complex, is a pure Markov process. For the most part both models give mathematically-equivalent closed-form solutions for a number of important system performance metrics. These metrics include throughput, latency, stability condition, CPU utilizations of interrupt handling and protocol processing, and CPU availability for user applications. The analysis yields insight into understanding and predicting the impact of system and network choices on the performance of interrupt-driven systems when subjected to light and heavy network loads. More importantly, our analytical work can also be valuable in improving host performance. The paper gives guidelines and recommendations to address design and implementation issues. Simulation and reported experimental results show that our analytical models are valid and give a good approximation

Two Analytical Models for Evaluating Performance of Gigabit Ethernet Hosts with Finite Buffer

Two analytical models are developed to study the impact of interrupt overhead on operating system performance of network hosts with limited-size or finite buffer. Under heavy network traffic such as that of Gigabit Ethernet, the system performance will be negatively affected due to interrupt overhead caused by incoming traffic. In particular, packet loss, excessive latency and significant degradation in system throughput can be experienced. Also, user applications may livelock as the CPU power is mostly consumed by interrupt handling and protocol processing. In this paper, we present and compare two analytical models that capture host behavior and evaluate its performance. The first model is based on Markov processes and queueing theory, while the second, which is more accurate but more complex, is a pure Markov process. The models yield equations for a number of important system performance metrics. These performance metrics include throughput, latency, packet loss, stability condition, CPU utilizations of interrupt handling and protocol processing, and CPU availability for user applications. Both models yield closed-form solutions and equations that are either mathematically equivalent or very closely matching. Our analysis yields insight into understanding and predicting the impact of system and network choices on the performance of interrupt-driven systems when subjected to light and heavy network loads. More importantly, our analytical work can also be valuable in improving host performance. The paper gives guidelines and recommendations to address design and implementation issues. Simulation and reported experimental results show that our analytical models are valid and give a good approximation

Two Analytical Models for Evaluating Performance of Gigabit Ethernet Hosts with Finite Buffer

Two analytical models are developed to study the impact of interrupt overhead on operating system performance of network hosts with limited-size or finite buffer. Under heavy network traffic such as that of Gigabit Ethernet, the system performance will be negatively affected due to interrupt overhead caused by incoming traffic. In particular, packet loss, excessive latency and significant degradation in system throughput can be experienced. Also, user applications may livelock as the CPU power is mostly consumed by interrupt handling and protocol processing. In this paper, we present and compare two analytical models that capture host behavior and evaluate its performance. The first model is based on Markov processes and queueing theory, while the second, which is more accurate but more complex, is a pure Markov process. The models yield equations for a number of important system performance metrics. These performance metrics include throughput, latency, packet loss, stability condition, CPU utilizations of interrupt handling and protocol processing, and CPU availability for user applications. Both models yield closed-form solutions and equations that are either mathematically equivalent or very closely matching. Our analysis yields insight into understanding and predicting the impact of system and network choices on the performance of interrupt-driven systems when subjected to light and heavy network loads. More importantly, our analytical work can also be valuable in improving host performance. The paper gives guidelines and recommendations to address design and implementation issues. Simulation and reported experimental results show that our analytical models are valid and give a good approximation

Two Analytical Models for Evaluating Performance of Gigabit Ethernet Hosts with Finite Buffer

Two analytical models are developed to study the impact of interrupt overhead on operating system performance of network hosts with limited-size or finite buffer. Under heavy network traffic such as that of Gigabit Ethernet, the system performance will be negatively affected due to interrupt overhead caused by incoming traffic. In particular, packet loss, excessive latency and significant degradation in system throughput can be experienced. Also, user applications may livelock as the CPU power is mostly consumed by interrupt handling and protocol processing. In this paper, we present and compare two analytical models that capture host behavior and evaluate its performance. The first model is based on Markov processes and queueing theory, while the second, which is more accurate but more complex, is a pure Markov process. The models yield equations for a number of important system performance metrics. These performance metrics include throughput, latency, packet loss, stability condition, CPU utilizations of interrupt handling and protocol processing, and CPU availability for user applications. Both models yield closed-form solutions and equations that are either mathematically equivalent or very closely matching. Our analysis yields insight into understanding and predicting the impact of system and network choices on the performance of interrupt-driven systems when subjected to light and heavy network loads. More importantly, our analytical work can also be valuable in improving host performance. The paper gives guidelines and recommendations to address design and implementation issues. Simulation and reported experimental results show that our analytical models are valid and give a good approximation

Optimization of energy efficiency in data and WEB hosting centers

Mención Internacional en el título de doctorThis thesis tackles the optimization of energy efficiency in data centers in terms of network and server utilization. For what concerns networking utilization the work focuses on Energy Efficient Ethernet (EEE) - IEEE 802.3az standard - which is the energy-aware alternative to legacy Ethernet, and an important component of current and future green data centers. More specifically the first contribution of this thesis consists in deriving and analytical model of gigabit EEE links with coalescing using M/G/1 queues with sleep and wake-up periods. Packet coalescing has been proposed to save energy by extending the sojourn in the Low Power Idle state of EEE. The model presented in this thesis approximates with a good accuracy both the energy saving and the average packet delay by using a few significant traffic descriptors. While coalescing improves by far the energy efficiency of EEE, it is still far from achieving energy consumption proportional to traffic. Moreover, coalescing can introduce high delays. To this extend, by using sensitivity analysis the thesis evaluates the impact of coalescing timers and buffer sizes, and sheds light on the delay incurred by adopting coalescing schemes. Accordingly, the design and study of a first family of dynamic algorithms, namely measurement-based coalescing control (MBCC), is proposed. MBCC schemes tune the coalescing parameters on-the-fly, according to the instantaneous load and the coalescing delay experienced by the packets. The thesis also discusses a second family of dynamic algorithms, namely NT-policy coalescing control (NTCC), that adjusts the coalescing parameters based on the sole occurrence of timeouts and buffer fill-ups. Furthermore, the performance of static as well as dynamic coalescing schemes is investigated using real traffic traces. The results reported in this work show that, by relying on run-time delay measurements, simple and practical MBCC adaptive coalescing schemes outperform traditional static and dynamic coalescing while the adoption of NTCC coalescing schemes has practically no advantages with respect to static coalescing when delay guarantees have to be provided. Notably, MBCC schemes double the energy saving benefit of legacy EEE coalescing and allow to control the coalescing delay. For what concerns server utilization, the thesis presents an exhaustive empirical characterization of the power requirements of multiple components of data center servers. The characterization is the second key contribution of this thesis, and is achieved by devising different experiments to stress server components, taking into account the multiple available CPU frequencies and the presence of multicore servers. The described experiments, allow to measure energy consumption of server components and identify their optimal operational points. The study proves that the curve defining the minimal CPU power utilization, as a function of the load expressed in Active Cycles Per Second, is neither concave nor purely convex. Instead, it definitively shows a superlinear dependence on the load. The results illustrate how to improve the efficiency of network cards and disks. Finally, the accuracy of the model derived from the server components consumption characterization is validated by comparing the real energy consumed by two Hadoop applications - PageRank and WordCount - with the estimation from the model, obtaining errors below 4:1%, on average.This work has been partially supported by IMDEA Networks Institute and the Greek State Scholarships FoundationPrograma Oficial de Doctorado en Ingeniería TelemáticaPresidente: Marco Giuseppe Ajmone Marsan.- Secretario: Jose Luis Ayala Rodrigo.- Vocal: Gianluca Antonio Rizz

Two Analytical Models (with Infinite Buffer) for Evaluating Performance of Gigabit Ethernet Hosts

Two analytical models are developed to study the impact of interrupt overhead on operating system performance of network hosts when subjected to Gigabit network traffic. Under heavy network traffic, the system performance will be negatively affected due to interrupt overhead caused by incoming traffic. In particular, excessive latency and significant degradation in system throughput can be experienced. Also, user applications may livelock as the CPU power is mostly consumed by interrupt handling and protocol processing. In this paper, we present and compare two analytical models that capture host behavior and evaluate its performance. The first model is based on Markov processes and queueing theory, while the second, which is more accurate but more complex, is a pure Markov process. For the most part both models give mathematically-equivalent closed-form solutions for a number of important system performance metrics. These metrics include throughput, latency, stability condition, CPU utilizations of interrupt handling and protocol processing, and CPU availability for user applications. The analysis yields insight into understanding and predicting the impact of system and network choices on the performance of interrupt-driven systems when subjected to light and heavy network loads. More importantly, our analytical work can also be valuable in improving host performance. The paper gives guidelines and recommendations to address design and implementation issues. Simulation and reported experimental results show that our analytical models are valid and give a good approximation

W-NINE: a two-stage emulation platform for mobile and wireless systems

More and more applications and protocols are now running on wireless networks. Testing the implementation of such applications and protocols is a real challenge as the position of the mobile terminals and environmental effects strongly affect the overall performance. Network emulation is often perceived as a good trade-off between experiments on operational wireless networks and discrete-event simulations on Opnet or ns-2. However, ensuring repeatability and realism in network emulation while taking into account mobility in a wireless environment is very difficult. This paper proposes a network emulation platform, called W-NINE, based on off-line computations preceding online pattern-based traffic shaping. The underlying concepts of repeatability, dynamicity, accuracy and realism are defined in the emulation context. Two different simple case studies illustrate the validity of our approach with respect to these concepts

Evaluating System Performance in Gigabit Networks

With the current wide deployment of Gigabit Ethernet technology in the backbone and workgroup switches, the network performance bottleneck has shifted for the first time in nearly a decade from the network to the end hosts and servers. This dramatic bandwidth increase calls for optimizations and good design considerations in many key components of the hosts and servers. These key components include network adaptor, operating system, protocol stack, memory, and processing power. More importantly the high bandwidth increase can negatively impact the OS performance due to the interrupt overhead caused by the incoming gigabit traffic. This paper presents models and analytical techniques for studying such a negative impact. We first present an analytical model for the ideal system when interrupt overhead is ignored. We then present two models which describe the impact of high interrupt rate on system throughput. One model is for network adaptors not equipped with DMA engines, and the other model is for network adaptors equipped with DMA engines. In addition we study the system performance when using different system delivery options of packet data to user applications. Results from both simulations and reported experimental findings show that our analytical models are valid and give a good approximation

Evaluating System Performance in Gigabit Networks

With the current wide deployment of Gigabit Ethernet technology in the backbone and workgroup switches, the network performance bottleneck has shifted for the first time in nearly a decade from the network to the end hosts and servers. This dramatic bandwidth increase calls for optimizations and good design considerations in many key components of the hosts and servers. These key components include network adaptor, operating system, protocol stack, memory, and processing power. More importantly the high bandwidth increase can negatively impact the OS performance due to the interrupt overhead caused by the incoming gigabit traffic. This paper presents models and analytical techniques for studying such a negative impact. We first present an analytical model for the ideal system when interrupt overhead is ignored. We then present two models which describe the impact of high interrupt rate on system throughput. One model is for network adaptors not equipped with DMA engines, and the other model is for network adaptors equipped with DMA engines. In addition we study the system performance when using different system delivery options of packet data to user applications. Results from both simulations and reported experimental findings show that our analytical models are valid and give a good approximation