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  • 1.
    Bogdanov, Kirill
    KTH, School of Electrical Engineering and Computer Science (EECS), Communication Systems, CoS, Network Systems Laboratory (NS Lab).
    Enabling Fast and Accurate Run-Time Decisions in Geo-Distributed Systems: Better Achieving Service Level Objectives2018Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Computing services are highly integrated into modern society and used  by millions of people daily. To meet these high demands, many popular  services are implemented and deployed as geo-distributed applications on  top of third-party virtualized cloud providers. However, the nature of  such a deployment leads to variable performance. To deliver high quality  of service, these systems strive to adapt to ever-changing conditions by  monitoring changes in state and making informed run-time decisions, such  as choosing server peering, replica placement, and redirection of requests. In  this dissertation, we seek to improve the quality of run-time decisions made  by geo-distributed systems. We attempt to achieve this through: (1) a better  understanding of the underlying deployment conditions, (2) systematic and  thorough testing of the decision logic implemented in these systems, and (3)  by providing a clear view of the network and system states allowing services  to make better-informed decisions.  First, we validate an application’s decision logic used in popular  storage systems by examining replica selection algorithms. We do this by  introducing GeoPerf, a tool that uses symbolic execution and modeling to  perform systematic testing of replica selection algorithms. GeoPerf was used  to test two popular storage systems and found one bug in each.  Then, using measurements across EC2, we observed persistent correlation  between network paths and network latency. Based on these observations,  we introduce EdgeVar, a tool that decouples routing and congestion based  changes in network latency. This additional information improves estimation  of latency, as well as increases the stability of network path selection.  Next, we introduce Tectonic, a tool that tracks an application’s requests  and responses both at the user and kernel levels. In combination with  EdgeVar, it decouples end-to-end request completion time into three  components of network routing, network congestion, and service time.  Finally, we demonstrate how this decoupling of request completion  time components can be leveraged in practice by developing Kurma, a  fast and accurate load balancer for geo-distributed storage systems. At  runtime, Kurma integrates network latency and service time distributions to  accurately estimate the rate of Service Level Objective (SLO) violations, for  requests redirected between geo-distributed datacenters. Using real-world  data, we demonstrate Kurma’s ability to effectively share load among  datacenters while reducing SLO violations by a factor of up to 3 in high  load settings or reducing the cost of running the service by up to 17%. The  techniques described in this dissertation are important for current and future  geo-distributed services that strive to provide the best quality of service to  customers while minimizing the cost of operating the service.  

  • 2.
    Bogdanov, Kirill
    KTH, School of Information and Communication Technology (ICT), Communication Systems, CoS, Network Systems Laboratory (NS Lab).
    Latency Dataset for the paper "The Nearest Replica Can Be Farther Than You Think"2015Data set
  • 3.
    Bogdanov, Kirill
    KTH, School of Information and Communication Technology (ICT), Communication Systems, CoS, Network Systems Laboratory (NS Lab).
    Reducing Long Tail Latencies in Geo-Distributed Systems2016Licentiate thesis, monograph (Other academic)
    Abstract [en]

    Computing services are highly integrated into modern society. Millions of people rely on these services daily for communication, coordination, trading, and accessing to information. To meet high demands, many popular services are implemented and deployed as geo-distributed applications on top of third party virtualized cloud providers. However, the nature of such deployment provides variable performance characteristics. To deliver high quality of service, such systems strive to adapt to ever-changing conditions by monitoring changes in state and making run-time decisions, such as choosing server peering, replica placement, and quorum selection.

    In this thesis, we seek to improve the quality of run-time decisions made by geo-distributed systems. We attempt to achieve this through: (1) a better understanding of the underlying deployment conditions, (2) systematic and thorough testing of the decision logic implemented in these systems, and (3) by providing a clear view into the network and system states which allows these services to perform better-informed decisions.

    We performed a long-term cross datacenter latency measurement of the Amazon EC2 cloud provider. We used this data to quantify the variability of network conditions and demonstrated its impact on the performance of the systems deployed on top of this cloud provider.

    Next, we validate an application’s decision logic used in popular storage systems by examining replica selection algorithms. We introduce GeoPerf, a tool that uses symbolic execution and lightweight modeling to perform systematic testing of replica selection algorithms. We applied GeoPerf to test two popular storage systems and we found one bug in each.

    Then, using traceroute and one-way delay measurements across EC2, we demonstrated persistent correlation between network paths and network latency. We introduce EdgeVar, a tool that decouples routing and congestion based changes in network latency. By providing this additional information, we improved the quality of latency estimation, as well as increased the stability of network path selection.

    Finally, we introduce Tectonic, a tool that tracks an application’s requests and responses both at the user and kernel levels. In combination with EdgeVar, it provides a complete view of the delays associated with each processing stage of a request and response. Using Tectonic, we analyzed the impact of sharing CPUs in a virtualized environment and can infer the hypervisor’s scheduling policies. We argue for the importance of knowing these policies and propose to use them in applications’ decision making process.

  • 4.
    Bogdanov, Kirill
    et al.
    KTH, School of Information and Communication Technology (ICT), Communication Systems, CoS, Network Systems Laboratory (NS Lab).
    Peón-Quirós, Miguel
    Complutense University of Madrid.
    Maguire Jr., Gerald Q.
    KTH, School of Information and Communication Technology (ICT), Communication Systems, CoS, Radio Systems Laboratory (RS Lab).
    Kostic, Dejan
    KTH, School of Information and Communication Technology (ICT), Communication Systems, CoS, Network Systems Laboratory (NS Lab).
    The Nearest Replica Can Be Farther Than You Think2015In: Proceedings of the ACM Symposium on Cloud Computing 2015, Association for Computing Machinery (ACM), 2015, p. 16-29Conference paper (Refereed)
    Abstract [en]

    Modern distributed systems are geo-distributed for reasons of increased performance, reliability, and survivability. At the heart of many such systems, e.g., the widely used Cassandra and MongoDB data stores, is an algorithm for choosing a closest set of replicas to service a client request. Suboptimal replica choices due to dynamically changing network conditions result in reduced performance as a result of increased response latency. We present GeoPerf, a tool that tries to automate the process of systematically testing the performance of replica selection algorithms for geodistributed storage systems. Our key idea is to combine symbolic execution and lightweight modeling to generate a set of inputs that can expose weaknesses in replica selection. As part of our evaluation, we analyzed network round trip times between geographically distributed Amazon EC2 regions, and showed a significant number of daily changes in nearestK replica orders. We tested Cassandra and MongoDB using our tool, and found bugs in each of these systems. Finally, we use our collected Amazon EC2 latency traces to quantify the time lost due to these bugs. For example due to the bug in Cassandra, the median wasted time for 10% of all requests is above 50 ms.

  • 5.
    Bogdanov, Kirill
    et al.
    KTH, School of Information and Communication Technology (ICT), Communication Systems, CoS, Network Systems Laboratory (NS Lab).
    Peón-Quirós, Miguel
    Complutense University of Madrid.
    Maguire Jr., Gerald Q.
    KTH, School of Information and Communication Technology (ICT), Communication Systems, CoS, Radio Systems Laboratory (RS Lab).
    Kostić, Dejan
    KTH, School of Information and Communication Technology (ICT), Communication Systems, CoS, Network Systems Laboratory (NS Lab).
    Toward Automated Testing of Geo-Distributed Replica Selection Algorithms2015In: Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication, Association for Computing Machinery (ACM), 2015, p. 89-90Conference paper (Refereed)
    Abstract [en]

    Many geo-distributed systems rely on a replica selection algorithms to communicate with the closest set of replicas.  Unfortunately, the bursty nature of the Internet traffic and ever changing network conditions present a problem in identifying the best choices of replicas. Suboptimal replica choices result in increased response latency and reduced system performance. In this work we present GeoPerf, a tool that tries to automate testing of geo-distributed replica selection algorithms. We used GeoPerf to test Cassandra and MongoDB, two popular data stores, and found bugs in each of these systems.

  • 6.
    Bogdanov, Kirill
    et al.
    KTH, School of Information and Communication Technology (ICT), Communication Systems, CoS, Network Systems Laboratory (NS Lab).
    Reda, Waleed
    KTH, School of Information and Communication Technology (ICT), Communication Systems, CoS, Network Systems Laboratory (NS Lab). Université catholique de Louvain.
    Kostic, Dejan
    KTH, School of Information and Communication Technology (ICT), Communication Systems, CoS, Network Systems Laboratory (NS Lab).
    Maguire Jr., Gerald Q.
    KTH, School of Information and Communication Technology (ICT), Communication Systems, CoS.
    Canini, Marco
    KAUST.
    Kurma: Fast and Efficient Load Balancing for Geo-Distributed Storage Systems: Evaluation of Convergence and Scalability2018Report (Other academic)
    Abstract [en]

    This report provides an extended evaluation of Kurma, a practical implementation of a geo-distributed load balancer for backend storage systems. In this report we demonstrate the ability of distributed Kurma instances to accurately converge to the same solutions within 1% of the total datacenter’s capacity and the ability of Kurma to scale up to 8 datacenters using a single CPU core at each datacenter.

  • 7.
    Bogdanov, Kirill
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Communication Systems, CoS.
    Reda, Waleed
    KTH, School of Electrical Engineering and Computer Science (EECS), Communication Systems, CoS.
    Maguire Jr., Gerald Q.
    KTH, School of Electrical Engineering and Computer Science (EECS), Communication Systems, CoS.
    Kostic, Dejan
    KTH, School of Electrical Engineering and Computer Science (EECS), Communication Systems, CoS.
    Canini, M.
    Fast and accurate load balancing for geo-distributed storage systems2018In: SoCC 2018 - Proceedings of the 2018 ACM Symposium on Cloud Computing, Association for Computing Machinery (ACM), 2018, p. 386-400Conference paper (Refereed)
    Abstract [en]

    The increasing density of globally distributed datacenters reduces the network latency between neighboring datacenters and allows replicated services deployed across neighboring locations to share workload when necessary, without violating strict Service Level Objectives (SLOs). We present Kurma, a practical implementation of a fast and accurate load balancer for geo-distributed storage systems. At run-time, Kurma integrates network latency and service time distributions to accurately estimate the rate of SLO violations for requests redirected across geo-distributed datacenters. Using these estimates, Kurma solves a decentralized rate-based performance model enabling fast load balancing (in the order of seconds) while taming global SLO violations. We integrate Kurma with Cassandra, a popular storage system. Using real-world traces along with a geo-distributed deployment across Amazon EC2, we demonstrate Kurma’s ability to effectively share load among datacenters while reducing SLO violations by up to a factor of 3 in high load settings or reducing the cost of running the service by up to 17%.

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