Abstract not found

Data centers are often under-utilized due to over-provisioning as well as time-varying resource demands of typical enterprise applications. One approach to increase resource utilization is to consolidate applications in a shared infrastructure using virtualization. Meeting application-level quality of service (QoS) goals becomes a challenge in a consolidated environment as application resource needs differ. Furthermore, for multi-tier applications, the amount of resources needed to achieve their QoS goals might be different at each tier and may also depend on availability of resources in other tiers. In this paper, we develop an adaptive resource control system that dynamically adjusts the resource shares to individual tiers in order to meet application-level QoS goals while achieving high resource utilization in the data center. Our control system is developed using classical control theory, and we used a black-box system modeling approach to overcome the absence of first principle models for complex enterprise applications and systems. To evaluate our controllers, we built a testbed simulating a virtual data center using Xen virtual machines. We experimented with two multi-tier applications in this virtual data center: a twotier implementation of RUBiS, an online auction site, and a two-tier Java implementation of TPC-W. Our results indicate that the proposed control system is able to maintain high resource utilization and meets QoS goals in spite of varying resource demands from the applications.

Most well-managed web servers perform well most of the time. Occasionally, however, every popular web server experiences transient overload. An overloaded web server typically displays signs of its affliction within a few seconds. Work enters the web server at a greater rate than the web server can complete it, causing the number of connections at the server to build up. This implies large delays for clients accessing the server. This paper provides a systematic performance study of exactly what happens when a web server is run under transient overload, both from the perspective of the server and from the perspective of the client. Second, this paper proposes and evaluates a particular kernel-level solution for improving the performance of web servers under overload. The solution is based on SRPT connection scheduling. We show that SRPT-based scheduling improves overload performance across a variety of client and server-oriented metrics.

We study several transparent techniques for scaling dynamic content web sites, and we evaluate their relative impact when used in combination. Full transparency implies strong data consistency as perceived by the user, no modifications to existing dynamic content site tiers and no additional programming effort from the user or site administrator upon deployment.

Abstract — Dynamic capacity provisioning is a useful technique for handling the multi-time-scale variations seen in Internet workloads. In this paper, we propose a novel dynamic provisioning technique for multi-tier Internet applications that employs (i) a flexible queuing model to determine how much resources to allocate to each tier of the application, and (ii) a combination of predictive and reactive methods that determine when to provision these resources, both at large and small time scales. We propose a novel data center architecture based on virtual machine monitors to reduce provisioning overheads. Our experiments on a forty-machine Xen/Linux-based hosting platform demonstrate the responsiveness of our technique in handling dynamic workloads. In one scenario where a flash crowd caused the workload of a three-tier application to double, our technique was able to double the application capacity within five minutes, thus maintaining response time targets. Our technique also reduced the overhead of switching servers across applications from several minutes to less than a second, while meeting the performance targets of residual sessions.

In this paper we describe Quorum, a non-invasive approach to scalable quality-of-service provisioning that uses traffic shaping, admission control, and response monitoring at the border of an Internet site to ensure throughput and response time guarantees. We experimentally compare an implementation of Quorum both to hardware over-provisioning and to leading software approaches using real world workloads. Our results show that Quorum can enforce the same QoS guarantees as either of the compared approaches, while achieving better resource utilization than over-provisioning and without the application rewriting overhead required by intrusive software approaches. We also demonstrate that our implementation can successfully handle extreme situations such as sudden traffic surges, application misbehavior and node failures. Furthermore, we demonstrate the flexibility of Quorum by providing QoS guarantees for a complex and heterogeneous Internet service that cannot be implemented by other current software approaches. 1

In this paper we present Cataclysm, a comprehensive approach for handling extreme overloads in hosted Internet applications. The primary contribution of our work is to develop an overload control approach that brings together admission control, dynamic provisioning of platform resources, and adaptive degradation of QoS into one integrated system. Cataclysm provides several desirable features under overloads, such as preferential admission of important requests, the ability to handle diverse workloads, and revenue maximization at multiple time-scales via dynamic provisioning and size-based admission control. Cataclysm can transparently tradeoff the accuracy of its decision making with the intensity of the workload allowing it to handle incoming rates of several tens of thousands of requests /second. We implement a prototype Cataclysm hosting platform on a Linux cluster and demonstrate the benefits of our integrated approach using a variety of workloads.

We present a memory-aware load balancing (MALB) technique to dispatch transactions to replicas in a replicated database. Our MALB algorithm exploits knowledge of the working sets of transactions to assign them to replicas in such a way that they execute in main memory, thereby reducing disk I/O. In support of MALB, we introduce a method to estimate the size and the contents of transaction working sets. We also present an optimization called update filtering that reduces the overhead of update propagation between replicas. We show that MALB greatly improves performance over other load balancing techniques – such as round robin, least connections, and locality-aware request distribution (LARD) – that do not use explicit information on how transactions use memory. In particular, LARD demonstrates good performance for read-only static content Web workloads, but it gives performance inferior to MALB for database workloads as it does not efficiently handle large requests. MALB combined with update filtering further boosts performance over LARD. We build a prototype replicated system, called Tashkent+, with which we demonstrate that MALB and update filtering techniques improve performance of the TPC-W and RUBiS benchmarks. In particular, in a 16-replica cluster and using the ordering mix of TPC-W, MALB doubles the throughput over least connections and improves throughput 52 % over LARD. MALB with update filtering further improves throughput to triple that of least connections and more than double that of LARD. Our techniques exhibit super-linear speedup; the throughput of the 16-replica cluster is 37 times the peak throughput of a standalone database due to better use of the cluster’s memory.

Abstract — Today’s shared hosting platforms often employ virtualization to allow multiple enterprise applications with time-varying resource demands to share a common infrastructure in order to improve resource utilization. Meeting application-level quality of service (QoS) goals becomes a challenge in such an environment as enterprise applications often have a multi-tier architecture and complex interactions and dependencies among individual tiers. In addition, when the shared infrastructure becomes overloaded, appropriate resource control needs to be performed at these individual tiers in a coordinated fashion in order to provide differentiated services to co-hosted applications. In this paper, we present an adaptive multivariate controller that dynamically adjusts the resource shares to individual tiers of multiple applications in order to meet a specified level of service differentiation. The controller parameters are automatically tuned at runtime based on a quadratic cost function and a system model that is learned online using a recursive least-squares (RLS) method. To evaluate our controller design, we built a testbed hosting two instances of the RUBiS application, a multi-tier online auction web site, using Xen virtual machines. Our results indicate that our controller is able to meet given QoS differentiation targets between co-hosted applications while the total demand from these applications exceeds the capacities of the shared systems. I.

The typical workload in a database system consists of a mixture of multiple queries of different types, running concurrently and interacting with each other. Hence, optimizing performance requires reasoning about query mixes and their interactions, rather than considering individual queries or query types. In this paper, we show the significant impact that query interactions can have on workload performance. We present a new approach based on planning experiments and statistical modeling to capture the impact of query interactions. This approach requires no prior assumptions about the internal workings of the database system or the nature or cause of query interactions, making it portable across systems. As a concrete demonstration of the potential of capturing, modeling, and exploiting query interactions, we develop a novel interaction-aware query scheduler that targets report-generation workloads in Business Intelligence (BI) settings. Under certain assumptions, the schedule found by this scheduler is within a constant factor of optimal. An experimental evaluation with TPC-H queries on IBM DB2 demonstrates that our scheduler consistently outperforms (up to 4x) conventional schedulers that do not account for query interactions.