Performance and scalability of cloud applications are heavily dependent on those same characteristics in the underlying network. Several factors complicate cloud network evaluation. Most notable are 1) large scale, 2) a practically infinite set of application workloads, 3) a large set of potential metrics, 4) a large variety of available configurations within and across clouds and 5) a lack of visibility into the underlying network. There is a tremendous need for a cloud network evaluation methodology that overcomes these challenges, that is practical to apply and provides predictive results. We describe our work to develop such a methodology for cloud network evaluation and a tool (Euclid) that automates its application. Euclid leverages a set of widely used traffic generators to evaluate throughput and latency across a cloud network between endpoint pairs. Evaluation of these micro benchmarks between and among different sets of endpoints is coordinated to reflect several well-known application traffic patterns. Large sets of results are distilled into a tractable set of metrics and associated graphs that facilitate consumption of the results by cloud operators and users. We report results from applying Euclid to seventeen cloud configurations that span three public and one private cloud. The results demonstrate the practicality of our approach and highlight some interesting differences across clouds.

Growth in cloud computing motivates cloud networks that provide excellent performance and scalability. Improvements rely on the ability to measure these characteristics. Measurement is complicated by a combinatorial explosion of implementations, configurations, metrics, workloads and scenarios. We present tools and a framework that facilitate performance and scalability evaluation. We present the results of applying the framework to six OpenStack network implementations/configurations. The results validate the framework's ability to highlight the performance and scalability impact of changes to the underlying cloud implementation and configuration. Our experience also yields important lessons regarding cloud network evaluation.

Instant messaging (IM) has become increasingly popular due to its quick response time, its ease of use, and possibility of multitasking. It is estimated that there are several millions of instant messaging users who use IM for various purposes: simple requests and responses, scheduling face to face meetings, or just to check the availability of colleagues and friends. Despite its popularity and user base, little has been done to characterize IM traffic. One reason might be its relatively small traffic volume, although this is changing as more users start using video or voice chats and file attachments. Moreover, all major instant messaging systems route text messages through central servers. While this facilitates firewall traversal and gives instant messaging companies more control, it creates a potential bottleneck at the instant messaging servers. This is especially so for large instant messaging operators with tens of millions of users and during flash crowd events. Another reason for the lack of previous studies is the difficulty in getting access to instant messaging traces due to privacy concerns. In this paper, we analyze the traffic of two popular instant messaging systems, AOL Instant Messenger (AIM) and MSN/Windows Live Messenger, from thousands of employees in a large enterprise. We found that most instant messaging traffic is due to presence, hints, or other extraneous traffic. Chat messages constitute only a small percentage of the total IM traffic. This means, during overload, IM servers can protect the instantaneous nature of the communication by dropping extraneous traffic. We also found that the social network of lM users does not follow a power law distribution. It can be characterized by a Weibull distribution. Our analysis sheds light on instant messaging system design and optimization and provides a scientific basis for instant messaging workload generation.

SIP is a protocol of growing importance, with uses for VoIP, instant messaging, presence, and more. However, its performance is not well-studied or understood. In this extended abstract we overview our experimental evaluation of common SIP server scenarios using open-source SIP software such as OpenSER and SIP prunning on Linux.

This paper examines the performance of simultaneous multithreading (SMT) for network servers using actual hardware, multiple network server applications, and several workloads. Using three versions of the Intel Xeon processor with Hyper-Threading, we perform macroscopic analysis as well as microarchitectural measurements to understand the origins of the performance bottlenecks for SMT processors in these environments. The results of our evaluation suggest that the current SMT support in the Xeon is application and workload sensitive, and may not yield significant benefits for network servers.In general, we find that enabling SMT on real hardware usually produces only slight performance gains, and can sometimes lead to performance loss. In the uniprocessor case, previous studies appear to have neglected the OS overhead in switching from a uniprocessor kernel to an SMT-enabled kernel. The performance loss associated with such support is comparable to the gains provided by SMT. In the 2-way multiprocessor case, the higher number of memory references from SMT often causes the memory system to become the bottleneck, offsetting any processor utilization gains. This effect is compounded by the growing gap between processor speeds and memory latency. In trying to understand the large gains shown by simulation studies, we find that while the general trends for microarchitectural behavior agree with real hardware, differences in sizing assumptions and performance models yield much more optimistic benefits for SMT than we observe.

This paper presents a method for admission control and request scheduling for multiply-tiered e-commerce Web sites, achieving both stable behavior during overload and improved response times. Our method externally observes execution costs of requests online, distinguishing different request types, and performs overload protection and preferential scheduling using relatively simple measurements and a straight forward control mechanism. Unlike previous proposals, which require extensive changes to the server or operating system, our method requires no modifications to the host O.S., Web server, application server or database. Since our method is external, it can be implemented in a proxy. We present such an implementation, called Gatekeeper, using it with standard software components on the Linux operating system. We evaluate the proxy using the industry standard TPC-W workload generator in a typical three-tiered e-commerce environment. We show consistent performance during overload and throughput increases of up to 10 percent. Response time improves by up to a factor of 14, with only a 15 percent penalty to large jobs.

In order to exploit the latest advances in hardware technology, application developers need high-performance, easy-to-use cooperation tools that spun interconnections of standard hardware. Distributed shared memory has been proposed as such a cooperation tool, but performance problems have limited its usefulness. This paper argues that a new approach to distributed shared memory implementation can make it an effective tool in its own right and a foundation for other tools. It describes a prototype implementation that allows sharing of memory resources in workstation cluster. The prototype is based on an innovative, low-overhead messaging protocol which utilizes the high bandwidth of the underlying hardware, while adding very little latency overhead. Finally, the interface exported by this software is designed to function effectively as a substrate for a variety of cooperation tools.>