Java Modelling Tools (JMT) is a suite of performance evaluation tools based on queueing network models. Recently {\em JSIMgraph}, the JMT discrete-event simulation tool, has been extended to express features of current computing systems such as Big data applications. The goal of this demonstration is to showcase novel support in JMT for fork-join synchronization, dynamic scaling of parallelism levels, memory and group capacity constraints.

In this paper, we present ParSim, a software tool designed for modeling parallel applications. The tool reproduces a workload, including its parallel characteristics specified by the users, on the target multicore or multiCPU system, and analyzes the collected data. The objective of ParSim is to close the gap between the "generalized" benchmarks, designed for conventional system architectures, and the peculiar needs of parallel environments based on multicore systems. A simple language for the description of parallel features of an application and specific analysis techniques are provided. We present the working context and principles of ParSim, its architecture and its features, and validate our tool through its application on a case study.

Today's web services are commonly hosted on clusters of servers that are often located within computing clouds, whose computational and storage resources can be highly heterogeneous. The workload served typically exhibits disparate computation patterns (e.g., CPU-intensive or IO-intensive), that fluctuate both in terms of volume and mix. The system heterogeneity together with workload diversity further exacerbates the challenge of effective distribution of load within a computing cloud. This paper presents a novel, mix-aware load-balancing algorithm, which aims to distribute requests sent by multiple applications in heterogeneous servers such that the application response times are minimized and system resources (e.g., CPU and IO) are equally utilized. To this end, the presented algorithm tries to not only balance the total number of requests seen by each server, but also to shape the requests received by each server into a certain "mix", that is analytically shown to be optimal for response time minimization. Our experimental results---based both on simulation and on a prototype implementation---show that the mix-aware algorithm achieves robust performance in most workload mixes as well as a consistent performance improvement in comparison with one of the most robust load-balancing schemes of the Apache server.

The transmission capacity of today's high-speed networks is often greater than the capacity of an end-system (such as a server or a remote client) to consume the incoming data. The mismatch between the network and the end-system, which can be exacerbated by high end-system workloads, will result in incoming packets being dropped at different points in the packet receiving process. In particular, a packet may be dropped in the NIC, in the kernel ring buffer, and (for rate based protocols) in the socket buffer. To provide reliable data transfers, these losses require retransmissions, and if the loss rate is high enough result in longer download times. In this paper, we focus on UDP-like rate based transport protocols, and address the question of how best to estimate the rate at which the end-system can consume data which minimizes the overall transfer time of a file.

Java Modelling Tools (JMT) is a suite of open source applications for performance evaluation and workload characterization of computer and communication systems based on queueing networks. JMT includes tools for workload characterization (JWAT), solution of queueing networks with analytical algorithms (JMVA), simulation of general-purpose queueing models (JSIM), bottleneck identification (JABA), and teaching support for Markov chain models underlying queueing systems (JMCH). This tutorial summarizes the main features of the tools that compose the suite. Furthermore, using a composite case study, we provide intuition on the versatility of JMT in dealing with the different aspects of quality-of-service (QoS) evaluation, what-if analysis, and software performance tuning.

This work summarizes our research on the topic of the application of unsupervised learning algorithms to the problem of intrusion detection, and in particular our main research results in network intrusion detection. We proposed a novel, two tier architecture for network intrusion detection, capable of clustering packet payloads and correlating anomalies in the packet stream. We show the experiments we conducted on such architecture, we give performance results, and we compare our achievements with other comparable existing systems.

We propose the Geometric Bounds (GBs), a new family of fast and accurate noniterative bounds on closed queueing network performance metrics that can be used in the online optimization of distributed applications. Compared to state-of-the-art techniques such as the Balanced Job Bounds (BJBs), GB achieves higher accuracy at similar computational costs, limiting the worst- case bounding error typically within 5-13 percent when, for the BJB, it is usually in the range of 15-35 percent. Optimization problems that are solved with GBs return solutions that are much closer to the global optimum than with existing bounds. We also show that the GB technique generalizes as an accurate approximation to closed fork-join networks commonly used in disk, parallel, and database models, thus extending the applicability of the method beyond the optimization of basic product-form networks.

A new emerging class of problems related to the online configuration and optimization of computer systems and networks requires the solution in a very short amount of time of a large number of analytical performance models, often based on queueing networks. In this paper we propose the Geometric Bounds (GB), a new family of fast noniterative bounds on performance metrics of closed productform queueing networks. In spite of their simplicity, the proposed bounds are more accurate than the popular Balanced Job Bounds (BJB), even in the difficult case of networks with multiple bottlenecks or large delays.

While triggers have become a classical ingredient of relational database systems, research in active databases is aiming at extending the functionality and expressive power of active rules beyond the scope of relational triggers. One of the most important current trend concerns the support of detached active rules, i.e., of rules which are executed as separate transactions, running outside of the scope of the transaction which generates the triggering event. Detached rules have important applications in workflow management and global integrity maintenance across transactions. One of the main issues in designing the rule engine for detached rules is determining their optimal scheduling. In this paper, we study the performance of a detached rule scheduler whose objective is to minimize the interference of detached rule execution with regard to the normal transactional load. This objective is achieved by executing detached rules at given periods of time and by assigning them a fixed amount of dedicated threads; we study the performance of the scheduler relative to the two most critical design parameters, the frequency of execution of the scheduler, and the number of dedicated execution threads.

Queuing network models of modern computing systems must consider a large number of components (e.g., Web servers, DB servers, application servers, firewall, routers, networks) and hundreds of customers with very different resource requirements. The complexity of such models makes the application of exact solution techniques prohibitively expensive, motivating research on approximate methods. This paper proposes an interpolation-matching framework that allows a unified view of approximate solution techniques for closed product-form queuing networks. Depending upon the interpolating functional form and the matching populations selected, a large versatile family of new approximations can be generated. It is shown that all the known approximation strategies, including Linearizer, are instances of the interpolation-matching framework. Furthermore, a new approximation technique, based on a third-order polynomial, is obtained using the interpolation-matching framework. The new technique is shown to be more accurate than other known methods.

A fundamental understanding of the interplay between computation and I/O activities in parallel applications that manipulate huge amounts of data is critical to achieving good application performance, as well as correctly characterizing the workloads of large-scale high-performance parallel systems. We present a formal model of the behavior of CPU and I/O interactions in scientific applications, from which we derive various formulas that characterize application performance. Our model captures the I/O and CPU activity at different levels of granularity, where results from the model are shown to be in excellent agreement with measurement data from a set of I/O-intensive applications. Using the formulas from our model, which explicitly take I/O activity into account, we also present examples of possible applications of the model.

Distributed application systems are composed of classes of objects with instances that interact to accomplish common goals. Such systems can have many classes of users with many types of requests. Furthermore, the relative load of these classes can shift throughout the day, causing changes to system behavior and bottlenecks. When designing and deploying such systems, it is necessary to determine a process replication and threading policy for the server processes that contain the objects, as well as process activation policies. To avoid bottlenecks, the policy must support all possible workload conditions. Licensing, implementation or resource constraints can limit the number of permitted replicas or threads of a server process. Process activation policies determine whether a server is persistent or should be created and terminated with each call. This paper describes quantitative techniques for choosing process replication or threading levels and process activation policies. Inappropriate policies can lead to unnecessary queuing delays for callers or unnecessarily high consumption of memory resources. The algorithms presented consider all workload conditions, are iterative in nature and are hybrid mathematical programming and analytic performance evaluation methods. An example is given to demonstrate the technique and describe how the results can be applied during software design and deployment.

The load generated by new types of communications services related to multimedia and video transmission is becoming one of the major sources of traffic in WAN networks. Modeling this type of load is a prerequisite for any performance study. We approach the load-characterization problem from a global point of view by analyzing a set of 20 video streams. We developed resource-, subject-, and scene-oriented characterizations of coded video streams. We have also implemented multidimensional data-analysis techniques and applied a "scene working set" approach, as well as static and dynamic video traffic models. We show that the behavior of a video sequence can be predicted with high accuracy by applying the scene working set technique. Applications of this technique for predicting bandwidth demands and allocating buffers in an ATM switch are also described in this study.

In this paper we systematically examine various performance issues involved in the coordinated allocation of processor and disk resources in large-scale parallel computer systems. Models are formulated to investigate the I/O and computation behavior of parallel programs and workloads, and to analyze parallel scheduling policies under such workloads. These models are parameterized by measurements of parallel programs, and they are solved via analytic methods and simulation. Our results provide important insights into the performance of parallel applications and resource management strategies when I/O demands are not negligible.

In this paper, processor scheduling policies that "save" processors are introduced and studied. In a multiprogrammed parallel system, a "processor saving" scheduling policy purposefully keeps some of the available processors idle in the presence of work to be done. The conditions under which processor saving policies can be more effective than their greedy counterparts, i.e., policies that never leave processors idle in the presence of work to be done, are examined. Sensitivity analysis is performed with respect to application speedup, system size, coefficient of variation of the applications' execution time, variability in the arrival process, and multiclass workloads. Analytical, simulation, and experimental results show that processor saving policies outperform their greedy counterparts under a variety of system and workload characteristics.

Some peculiar properties of multi-class product-form closed queueing networks with two classes of customers are presented. In particular, we show that in a network with two stations there exists a combination of customer classes such that the stations are equally utilized regardless of the population size. Such an equiutilization point is shown to be an optimal operational point at which the system operates at maximum power. Furthermore, it is shown that the equiutilization point belongs to a set of customer populations that lead both stations to saturate simultaneously as the population increases. The edges of such a common saturation sector are also derived and the limit values of the performance measures when the population increases to infinity are obtained.

This paper presents a simulation analysis of the impact of mobility on TCP/IP augmented with features to support host mobility in wide area networks. Our results show that the existing version of TCP can yield low throughput in highly mobile environments due to the fact that TCP cannot discriminate packets dropped due to hand-offs with those dropped due to congestion in one or more network resources. As a result, TCP invokes a congestion recovery process when packets are lost during internetwork hand-offs of the mobile host. We investigate a proposal in which the transport layer explicitly receives information from the network layer of any ongoing mobility. We show that by effectively capitalizing this information, TCP can appropriately extend the slow-start phase in the recovery process and achieve higher throughput. Based on the simulation analysis we also show the robustness of this scheme in the presence of both host mobility and network congestion.>

This paper presents a simulation analysis of the impact of mobility on data transport protocols. We consider the TCP/IP protocol augmented with features to support host mobility in wide area networks. Our results show that the existing version of TCP can yield low throughput in highly mobile environments due to the fact that TCP cannot discriminate packets dropped due to hand-offs with those dropped due to congestion in one or more network resources. We investigate a proposal in which the transport layer explicitly receives information from the network layer of any ongoing mobility. We show that by effectively capitalizing this information, TCP can appropriately extend the slow-start phase in the recovery process and achieve higher throughput. Based on the simulation analysis we also show the robustness of this scheme in the presence of both host mobility and network congestion.>

The performance of a system is determined by its characteristics as well as by the composition of the load being processed. Hence, its quantitative description is a fundamental part of all performance evaluation studies. Several methodologies for the construction of workload models, which are functions of the objective of the study, of the architecture of the system to be analyzed, and of the techniques adopted, are presented. A survey of a few applications of these methodologies to various types of systems (i.e., batch, interactive, database, network-based, parallel, supercomputer), is given.>

The concept of a processor working set (PWS) as a single value parameter for characterizing the parallel program behavior is introduced. Through detailed experimental studies of different algorithms on a transputer-based multiprocessor machine, it is shown that the PWS is a robust measure for characterizing the workload of a multiprocessor system. It is shown that processor allocation strategies based on the PWS provide significantly better throughput-delay characteristics. The robustness of PWS is further demonstrated by showing that allocation policies that allocate processors more than the PWS are inferior in performance to those that never allocate more than the PWS-even at a moderately low load. Based on the results, a simple static allocation policy that allocates the PWS at low load and adaptively fragments at high load to one processor per job is proposed.>

The knowledge of workload fluctuations is fundamental in all performance studies in which the dynantic characteristics of the resource demands must be taken into account. Among the several workload data sequences that may be considered, the arrival pattern of workload components is certainly one of the most important. An approach to the identification of the arrival rate functions through a numerical fitting technique that allows one to have concise representations of the arrival patterns during one-day periods is presented. The variability of the arrival pattern over different days is also investigated. The patterns which may be considered as "representatives" of the analyzed workload are found through the application of the clustering technique. The parametric model of the arrival process may easily be used to forecast the load of the system in the near future or to drive system simulations when several dynamic control policies are to be investigated. The proposed modeling approach has been validated, in the sense of establishing its predictive value, through the analysis of workload data for the same installation collected during different periods.

Applications of modeling techniques based on queueing theory to computer system performance analysis normally assume the existence of steady-state conditions. However, these conditions are often violated since the unpredictable composition of workload causes peaks having highly variable intensities and durations. Furthermore, computer system performance is highly dependent on how the system reacts to workload fluctuations. Automatic control mechanisms are required to take care of the high variance of resource demands. Real-time optimization of the overall performance of a computer system requires the introduction of adaptive control on the controlled functions, An adaptive scheduling algorithm which controls the input of the system in order to maximize a given performance criterion, such as the system throughput, is presented. The system load is adjusted depending on the characteristics of both the mix of jobs in execution and the mix of jobs submitted to the system and waiting in the input queue. The asymptotic analysis of the performance bounds provides useful information about the limits on the performance indexes that can be achieved with a multiclass workload. The evaluation of the adaptive control system is performed through simulation experiments using data collected from two real workloads. This technique could be used to optimize the throughput of a centralized system as well as for the automatic load balancing in a distributed environment.