With the current COVID-19 pandemic, contact tracing and building occupancy tracking are key components of re-opening policies and quickly containing virus outbreaks. WiFiMon is a network-centric contact tracing method that uses enterprise WiFi networks logs for tracking devices and inferring building occupancy and building contact tracing reports in office and campus settings.

Multi-Path TCP (MPTCP) is a new transport protocol that enables systems to exploit available paths through multiple network interfaces. MPTCP is particularly useful for mobile devices, which usually have multiple wireless interfaces. However, these devices have limited power capacity and thus judicious use of these interfaces is required.

In this demonstration, we present an open, flexible and realistic benchmarking platform named Video BenchLab to measure the performance of streaming media workloads. While Video BenchLab can be used with any existing media server, we provide a set of tools for researchers to experiment with their own platform and protocols. The components include a MediaDrop video server, a suite of tools to bulk insert videos and generate streaming media workloads, a dataset of freely available video and a client runtime to replay videos in the native video players of real Web browsers such as Firefox, Chrome and Internet Explorer. Various metrics are collected to capture the quality of video playback and identify issues that can happen during video replay. Finally, we provide a Dashboard to manage experiments, collect results and perform analytics to compare performance between experiments.

In this paper, we present an open, flexible and realistic benchmarking platform named Video BenchLab to measure the performance of streaming media workloads. While Video BenchLab can be used with any existing media server, we provide a set of tools for researchers to experiment with their own platform and protocols. The components include a MediaDrop video server, a suite of tools to bulk insert videos and generate streaming media workloads, a dataset of freely available video and a client runtime to replay videos in the native video players of real Web browsers such as Firefox, Chrome and Internet Explorer. We define simple metrics that are able to capture the quality of video playback and identify issues that can happen during video replay. Finally, we provide a Dashboard to manage experiments, collect results and perform analytics to compare performance between experiments.

In this paper, we present mBenchLab, a software infrastructure to measure the Quality of Experience (QoE) on tablet and smartphones accessing cloud hosted Web services. mBenchLab does not rely on emulation but uses real phones and tablets with their original software stack and communication interfaces for performance evaluation. We have used mBenchLab to measure the QoE of well-known web sites on various devices (Android tablets and smartphones) and networks (Wifi, 3G, 4G). We present our experimental results and lessons learned measuring QoE on mobile devices with mBenchLab. In our QoE analysis, we were able to discover a new bug in a very popular smartphone that impacts both performance and data usage. We have also made the entire mBenchLab software available as open source to the community to measure QoE on mobile devices that access cloud-hosted Web applications.

Peer-to-peer networks are the most popular mechanism for the criminal acquisition and distribution of child pornography (CP). In this paper, we examine observations of peers sharing known CP on the eMule and Gnutella networks, which were collected by law enforcement using forensic tools that we developed. We characterize a year's worth of network activity and evaluate different strategies for prioritizing investigators' limited resources. The highest impact research in criminal forensics works within, and is evaluated under, the constraints and goals of investigations. We follow that principle, rather than presenting a set of isolated, exploratory characterizations of users.

The high replication cost of Byzantine fault-tolerance (BFT) methods has been a major barrier to their widespread adop-tion in commercial distributed applications. We present ZZ, a new approach that reduces the replication cost of BFT ser-vices from 2f + 1 to practically f + 1. The key insight in ZZ is to use f + 1 execution replicas in the normal case and to activate additional replicas only upon failures. In data cen-ters where multiple applications share a physical server, ZZ reduces the aggregate number of execution replicas running in the data center, improving throughput and response times. ZZ relies on virtualization—a technology already employed in modern data centers—for fast replica activation upon fail-ures, and enables newly activated replicas to immediately be-gin processing requests by fetching state on-demand. A pro-totype implementation of ZZ using the BASE library and Xen shows that, when compared to a system with 2f + 1 repli-cas, our approach yields lower response times and up to 33% higher throughput in a prototype data center with four BFT web applications. We also show that ZZ can handle simulta-neous failures and achieve sub-second recovery. 1

Cloud computing platforms are becoming increasingly popular for e-commerce applications that can be scaled on-demand in a very cost effective way. Dynamic provisioning is used to autonomously add capacity in multi-tier cloud-based applications that see workload increases. While many solutions exist to provision tiers with little or no state in applications, the database tier remains problematic for dynamic provisioning due to the need to replicate its large disk state. In this paper, we explore virtual machine (VM) cloning techniques to spawn database replicas and address the challenges of provisioning shared-nothing replicated databases in the cloud. We argue that being able to determine state replication time is crucial for provisioning databases and show that VM cloning provides this property. We propose Dolly, a database provisioning system based on VM cloning and cost models to adapt the provisioning policy to the cloud infrastructure specifics and application requirements. We present an implementation of Dolly in a commercial-grade replication middleware and evaluate database provisioning strategies for a TPC-W workload on a private cloud and on Amazon EC2. By being aware of VM-based state replication cost, Dolly can solve the challenge of automated provisioning for replicated databases on cloud platforms.

Today's cloud computing platforms have seen much success in running compute-bound applications with time-varying or one-time needs. In this position paper, we will argue that the cloud paradigm is also well suited for handling data-intensive applications, characterized by the processing and storage of data produced by high-bandwidth sensors or streaming applications. The data rates and the processing demands vary over time for many such applications, making the on-demand cloud paradigm a good match for their needs. However, today's cloud platforms need to evolve to meet the storage, communication, and processing demands of data-intensive applications. We present an ongoing GENI project to connect high-bandwidth radar sensor networks with computational and storage resources in the cloud and use this example to highlight the opportunities and challenges in designing end-to-end data-intensive cloud systems.

This paper develops analytical models to predict through-put and response time of a replicated database using meas-urements of the workload on a standalone database. These models allow workload scalability to be estimated before the replicated system is deployed, making the technique useful for capacity planning and dynamic service provi-sioning. The models capture the scalability limits stemming from update propagation and aborts for both multi-master and single-master replicated databases that support snap-shot isolation.

Many data center virtualization solutions, such as VMware ESX, employ content-based page sharing to consolidate the resources of multiple servers. Page sharing identifies virtual machine memory pages with identical content and consolidates them into a single shared page. This technique, implemented at the host level, applies only between VMs placed on a given physical host. In a multi-server data center, opportunities for sharing may be lost because the VMs holding identical pages are resident on different hosts. In order to obtain the full benefit of content-based page sharing it is necessary to place virtual machines such that VMs with similar memory content are located on the same hosts.

Continuous "always-on" monitoring is beneficial for a number of applications, but potentially imposes a high load in terms of communication, storage and power consumption when a large number of variables need to be monitored. We introduce two new filtering techniques, swing filters and slide filters, that represent within a prescribed precision a time-varying numerical signal by a piecewise linear function, consisting of connected line segments for swing filters and (mostly) disconnected line segments for slide filters. We demonstrate the effectiveness of swing and slide filters in terms of their compression power by applying them to a real-life data set plus a variety of synthetic data sets. For nearly all combinations of signal behavior and precision requirements, the proposed techniques outperform the earlier approaches for online filtering in terms of data reduction. The slide filter, in particular, consistently dominates all other filters, with up to twofold improvement over the best of the previous techniques.

The need for high availability and performance in data management systems has been fueling a long running interest in database replication from both academia and industry. However, academic groups often attack replication problems in isolation, overlooking the need for completeness in their solutions, while commercial teams take a holistic approach that often misses opportunities for fundamental innovation. This has created over time a gap between academic research and industrial practice.

We consider a cluster architecture in which dynamic content is generated by a database back-end and a collection of Web and application server front-ends. We study the effect of transparent query caching on the performance of such a cluster. Transparency requires that cached entries be invalidated as a result of writes. We start with a coarse-grain table-level automatic invalidation cache. Based on observed workload characteristics, we enhance the cache with the necessary dependency tracking and invalidations at the finer granularity of columns. Finally we reduce the miss penalty of invalidations through full and partial coverage of query results.In terms of system design, a query cache may be located at the database back-end, on dedicated machines, on the front-ends, or on a combination thereof. This paper evaluates the tradeoffs of the different cache designs and the cache location using the TPC-W benchmark.Our experiments show that our transparent query cache improves performance very substantially by up to a factor of 1.5 in throughput and 4.2 in response time overall compared to the baseline table-based invalidation scheme. An important contributor to this end result, our optimization for reducing the miss penalty through full and partial coverage detection of query results from the cache improves response time by up to a factor of 2.9 compared to a cache with fine-grained column-based invalidations alone. Thus, the benefits of the higher hit ratio in our optimizations outweigh the costs of additional processing. The results are less clear-cut in terms of where to locate the cache. Performance differences when varying the cache location and the number of caches are small.

The report summarizes the results of the Workshop on Middleware Benchmarking held during OOPSLA 2003. The goal of the workshop was to help advance the current practice of gathering performance characteristics of middleware implementations through benchmarking. The participants of the workshop have focused on identifying requirements of and obstacles to middleware benchmarking and forming a position on the related issues. Selected requirements and obstacles are presented, together with guidelines to adhere to when benchmarking, open issues of current practice, and perspectives on further research. Copyright © 2005 John Wiley & Sons, Ltd.

Abstract Resource management in a Grid computing environment raises several technical issues. The monitoring infrastructure must be scalable, flexible, configurable and adaptable to support thousands of devices in a highly dynamic environment where operational conditions are constantly changing. We propose to address these challenges by combining asynchronous communications with reflective component‐based technologies. We introduce DREAM (Dynamic REflective Asynchronous Middleware), a Java component‐based message oriented middleware. Asynchronous communications are used to achieve the scalability and flexibility objectives, whereas the reflective component technology provides the complementary configurability and adaptability features. We argue that this infrastructure is a viable approach to build a resource monitoring infrastructure for Grid computing. Moreover, DREAM makes the monitoring logic accessible from J2EE application servers. This allows the monitoring information to be presented as a Grid service and to integrate within the Open Grid Software Architecture. Copyright © 2004 John Wiley & Sons, Ltd.

Distributed Shared Memories (DSM) performance has always suffered from high network latencies and software communication layers with a large overhead. Memory mapped networks such as Scalable Coherent Interface (SCI) allow to reliably access remote memory without involving the operating system. To show how DSM systems can benefit from this technology, we have developed SciFS, a DSM tightly integrated with the operating system, that exploits the high performance and the remote memory access capabilities of SCI. We first show the respective advantages of two communications techniques with SCI: programmed IO (PIO) and remote DMA (RDMA). Then, we describe how to build a scalable page transfer mechanism by mixing PIO and RDMA. Despite the lack of a broadcast mechanism with SCI, we demonstrate that it is possible to build scalable synchronization primitives using PIO. Finally, we evaluate various consistency models with scientific computing applications from the Splash benchmark. We observe that, even if the rough network performance is good, it is not sufficient to obtain acceptable results with applications that require fine grain parallelism. However, we show that memory mapped networks provide an efficient hardware support to implement software DSM systems without requiring complex relaxed consistency models. This way, DSM design can be greatly simplified using this technology.

We investigate the combined effect of application implementation method, container design, and efficiency of communication layers on the performance scalability of J2EE application servers by detailed measurement and profiling of an auction site server.We have implemented five versions of the auction site. The first version uses stateless session beans, making only minimal use of the services provided by the Enterprise JavaBeans (EJB) container. Two versions use entity beans, one with container-managed persistence and the other with bean-managed persistence. The fourth version applies the session fasade pattern, using session beans as a fasade to access entity beans. The last version uses EJB 2.0 local interfaces with the session fasade pattern. We evaluate these different implementations on two popular open-source EJB containers with orthogonal designs. JBoss uses dynamic proxies to generate the container classes at run time, making an extensive use of reflection. JOnAS pre-compiles classes during deployment, minimizing the use of reflection at run time. We also evaluate the communication optimizations provided by each of these EJB containers.The most important factor in determining performance is the application implementation method. EJB applications with session beans perform as well as a Java servlets-only implementation and an order-of-magnitude better than most of the implementations based on entity beans. The fine-granularity access exposed by the entity beans limits scalability. Use of session fasade beans improves performance for entity beans, but only if local communication is very efficient or EJB 2.0 local interfaces are used. Otherwise, session fasade beans degrade performance.For the implementation using session beans, communication cost forms the major component of the execution time on the EJB server. The design of the container has little effect on performance. With entity beans, the design of the container becomes important. In particular, the cost of reflection affects performance. For implementations using session fasade beans, local communication cost is critically important. EJB 2.0 local interfaces improve the performance by avoiding the communication layers for local communications.

The SIRAC laboratory has developed SciFS, a distributed shared memory (DSM) that tries to benefit from the high performance and the remote addressing capabilities of the scalable coherent interface (SCI) memory mapped network. We use SciFS for high performance cluster computing and we also experiment with it to build large scale clustered Web caches. We propose Whoops! a clustered Web cache prototype with a new request distribution algorithm, called PPBL (Parallel Pull-Based LRU), especially designed for use with a memory mapped network and a DSM system. Unlike other distribution algorithms the decision is distributed over all nodes thus providing better scalability. We evaluate a first PPBL implementation and discuss scalability issues. Then, we propose a solution to build a scalable implementation of PPBL. We conclude with other improvements that can be achieved to build efficient large scale clustered Web caches using a DSM over memory mapped networks.

In this paper we consider the use of a supercomputer with a hardware shared memory versus a cluster of workstations using a software Distributed Shared Memory (DSM). We focus on ray tracing applications to compare both architectures. We have ported Stingray, a parallel cone tracer developed on a SGI Origin 2000 supercomputer, on a cluster using a Scalable Coherent Interface (SCI) network and a software DSM called SciFS.