text: Long-running service workloads (e.g. web search engine) and short-term data analysis workloads (e.g. Hadoop MapReduce jobs) co-locate in today's data centers. Developing realistic benchmarks to reflect such practical scenario of mixed workload is a key problem to produce trustworthy results when evaluating and comparing data center systems. This requires using actual workloads as well as guaranteeing their submissions to follow patterns hidden in real-world traces. However, existing benchmarks either generate actual workloads based on probability models, or replay real-world workload traces using basic I O operations. To fill this gap, we propose a benchmark tool that is a first step towards generating a mix of actual service and data analysis workloads on the basis of real workload traces. Our tool includes a combiner that enables the replaying of actual workloads according to the workload traces, and a multi-tenant generator that flexibly scales the workloads up and down according to users' requirements. Based on this, our demo illustrates the workload customization and generation process using a visual interface. The proposed tool, called BigDataBench-MT, is a multi-tenant version of our comprehensive benchmark suite BigDataBench and it is publicly available from this http URL
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  @cite_7: Software techniques that tolerate latency variability are vital to building responsive large-scale Web services.
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  @cite_1: Elasticity (on-demand scaling) of applications is one of the most important features of cloud computing. This elasticity is the ability to adaptively scale resources up and down in order to meet varying application demands. To date, most existing scaling techniques can maintain applications' Quality of Service (QoS) but do not adequately address issues relating to minimizing the costs of using the service. In this paper, we propose an elastic scaling approach that makes use of cost-aware criteria to detect and analyse the bottlenecks within multi-tier cloud-based applications. We present an adaptive scaling algorithm that reduces the costs incurred by users of cloud infrastructure services, allowing them to scale their applications only at bottleneck tiers, and present the design of an intelligent platform that automates the scaling process. Our approach is generic for a wide class of multi-tier applications, and we demonstrate its effectiveness against other approaches by studying the behaviour of an example e-commerce application using a standard workload benchmark.
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  @cite_27: Reliability at massive scale is one of the biggest challenges we face at Amazon.com, one of the largest e-commerce operations in the world; even the slightest outage has significant financial consequences and impacts customer trust. The Amazon.com platform, which provides services for many web sites worldwide, is implemented on top of an infrastructure of tens of thousands of servers and network components located in many datacenters around the world. At this scale, small and large components fail continuously and the way persistent state is managed in the face of these failures drives the reliability and scalability of the software systems. This paper presents the design and implementation of Dynamo, a highly available key-value storage system that some of Amazon's core services use to provide an "always-on" experience. To achieve this level of availability, Dynamo sacrifices consistency under certain failure scenarios. It makes extensive use of object versioning and application-assisted conflict resolution in a manner that provides a novel interface for developers to use.
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  @cite_46: Cassandra is a distributed storage system for managing very large amounts of structured data spread out across many commodity servers, while providing highly available service with no single point of failure. Cassandra aims to run on top of an infrastructure of hundreds of nodes (possibly spread across different data centers). At this scale, small and large components fail continuously. The way Cassandra manages the persistent state in the face of these failures drives the reliability and scalability of the software systems relying on this service. While in many ways Cassandra resembles a database and shares many design and implementation strategies therewith, Cassandra does not support a full relational data model; instead, it provides clients with a simple data model that supports dynamic control over data layout and format. Cassandra system was designed to run on cheap commodity hardware and handle high write throughput while not sacrificing read efficiency.
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  @cite_20: Despite the importance of providing fluid responsiveness to user requests for interactive services, such request processing is very resource expensive when dealing with large-scale input data. These often exceed the application owners' budget when services are deployed on a cloud, in which resources are charged in monetary terms. Providing approximate processing results is a feasible solution for such problem that trades off request correctness (quantified by output quality) for response time reduction. However, existing techniques in this area either use partial input data or skip expensive computations to produce approximate results, thus resulting in large losses in output quality on a tight resource budget. In this paper, we propose SARP, a Synopsis-based Approximate Request Processing framework to produce approximate results with small correctness losses even using small amount of resources. To achieve this, SARP conducts full computations over the statistical aggregation of the entire input data using two key ideas: (1) offline synopsis management that generates and maintains a set of synopses that represent the statistical aggregation of original input data at different approximation levels. (2) Online synopsis selection that considers both the current resource allocation and the workload status so as to select the synopsis with the maximal length that can be processed within the required response time. We demonstrate the effectiveness of our approach by testing the recommendation services in E-commerce sites using a large, real-world dataset. Using prediction accuracy as the output quality, the results demonstrate: (i) SARP achieves significant response time reduction with very small quality losses compared to the exact processing results.(ii) Using the same processing time, SARP demonstrates a considerable reduction in quality loss compared to existing approximation techniques.
summary: . First of all, we classify data center benchmarks according to their targeted systems. We consider three popular camps of systems in today's data centers: (1) : the great prosperity of the Hadoop-centric systems in industry brings a wide diversity of systems (e.g. Spark @cite_38 , HBase @cite_13 , Hive @cite_43 and Impala @cite_48 ) on top of Hadoop MapReduce and HDFS @cite_14 as well as a wide range of benchmarks specifically designed for these systems. (2) : parallel DBMSs (e.g. MySQL @cite_44 and Oracle @cite_39 ) and NoSQL data stores (e.g. Amazon Dynamo @cite_27 , Cassandra @cite_46 and Linkedin Voldemort @cite_0 ) also widely exist in data centers. (3) : long-running web services such as Nutch search engine @cite_47 and multi-tier cloud applications @cite_1 are another important type of data center applications. These services usually have stringent response time requirement @cite_9 and their request processing is distributed into a large number of service components for parallel processing, thus the service latency is determined by the tail latency of these components @cite_7 @cite_20 .