Oracle Workload Intelligence

Telecom software systems are a major aspect of telecommunications networks used for managing, administrating, auditing and monitoring purposes. One of the most significant features is the performance of the telecom software system, since there is a great variety of network elements in use within built systems through which the telecom software interacts. Workload characterization is an essential methodology for simulating load usage for a given infrastructure that is exposed to a production environment. Hence, exploring the characteristics of workloads well leads an increase on system efficiency and performance. Nevertheless, workload characterization has not been well examined yet for Telecom Software System. In this paper, a new workload characterization architecture is proposed and implemented to model the workload and simulate the behavior of the network element. This architecture provides information regarding the accuracy of the workload models for more precise performance testing. Additionally, we verify our workload characterization results obtained from our architecture in a real software system. Performance results show that proposed workload model-based simulation environment is promising to predict the performance of the software systems.

The computer-assisted liquid chromatographic system (MCASYST) is developed for automated identification and analysis. The system has six main functions: retention prediction system, liquid chromatographic data base system, automated identification system, optimization of separation conditions, data loading program from UV multichannel detector, and UV spectral data base system. The performance and potential of this MCASYST system is evaluated for toxic compounds identification in poisoned human fluids.

Characterization and synthetic generation of streaming access workloads are fundamentally important to the evaluation of Internet streaming delivery systems. GISMO is a toolkit for the generation of synthetic streaming media objects and workloads that capture a number of characteristics empirically verified by recent measurement studies. These characteristics include object popularity, temporal correlation of request, seasonal access patterns, user session durations, user interactivity times, and variable bit rate (VBR) self-similarity and marginal distributions. The embodiment of these characteristics in Gismo enables the generation of realistic and scalable request streams for use in benchmarking and comparative evaluation of Internet streaming media delivery techniques. To demonstrate the usefulness of Gismo, we present a case study that shows the importance of various workload characteristics in evaluating the bandwidth requirements of proxy caching and server patching techniques.

Recent studies have shown that moderate to high data redundancy exists in primary storage systems, such as VM-based, enterprise and HPC storage systems, which indicates that the data deduplication technology can be used to effectively reduce the write traffic and storage space in such environments. However, our experimental studies reveal that applying data deduplication to primary storage systems will cause space contention in main memory and data fragmentation on disks. This is in part because applying data deduplication introduces significant index memory overhead to the existing system and in part because a file or block is split into multiple small data chunks that are often located in non-sequential locations on disks after deduplication. This fragmentation of data can cause a subsequent read operation to invoke many disk I/O requests, thus leading to performance degradation. The existing primary data deduplication schemes, such as iDedup[1], are to leverage spatial locality in that they only select the large requests to deduplicate and exclude the small requests (e.g., 4KB, 8KB or less) because the latter only account for a tiny fraction of the storage capacity requirement[2]. Moreover, these schemes tend to overlook the importance of cache management, leading them to manage the index cache and the read cache separately. However, previous workload studies on primary storage systems have revealed that small I/O requests dominate in the primary storage systems (more than 50%) and are at the root of the system performance bottleneck. Furthermore, the accesses in primary storage systems exhibit obvious I/O burstiness. The existing primary-storage data deduplication schemes fail to consider these workload characteristics in primary storage systems from the performance's perspective. We argue that, primary-storage data deduplication schemes should take the workload characteristics of primary storage into the design considerations. To address the two problems and take the primary-storage workload characteristics into considerations, we propose a Performance-Oriented I/O Deduplication approach, POD, to improving the I/O performance of primary storage systems in the Cloud. POD takes a two-pronged deduplication approach to improve primary storage systems, a request-based I/O and data deduplication scheme, called Select-Dedupe, aimed at alleviating data fragmentation and an adaptive memory management scheme, called iCache, to ease the main memory contention. More specifically, the former takes the workload characteristics of small-I/O-request domination into the design considerations. It deduplicates all the write requests if their write data is already stored sequentially on disks, including the small write requests that would otherwise be excluded from by the capacity-oriented deduplication schemes. For other write requests, Select-Dedupe does not deduplicate their redundant write data to maintain the performance of the subsequent read requests to these data. iCache takes the I/O burstiness characteristics into the design considerations. It dynamically adjusts the cache space between the index cache and the read cache according to the workload characteristics, and swaps these data between memory and backend storage devices accordingly. During the write-intensive bursty periods, iCache enlarges the index cache size and shrinks the read cache size to detect much more redundant write requests, thus improving the write performance. The read cache size is enlarged to cache more hot read data to improve the read performance during the read-intensive bursty periods. The prototype of the POD scheme is implemented as an embedded module at the block-device level with the fixed-size chunking method. Preliminary evaluations driven by the real traces conducted on our lightweight POD prototype implementation show that POD significantly outperforms iDedup in improving the performance of primary storage systems in the Cloud.

Graphics processing units have proved their capability for general-purpose computing in many research areas. In this paper, we propose the mechanism and implementation of a database system that encrypts and decrypts data by using GPU. The proposed mechanism is mainly designed for database systems that require data encryption and decryption to support high security level. The outsourced database systems or database cloud service could be a good candidate for our system. By exploiting the computation capability of GPU, we achieve not only a fast encryption and decryption time per operation, but also a higher overall performance of a database system by offloading computation to GPU. Moreover, the proposed system includes a mechanism which can decide whether to offload computation to GPU or not for more performance gain. We implemented the AES algorithm based on CUDA framework and integrate with MySQL, a commodity database system. Our evaluation demonstrates that the encryption and decryption on GPU show eight times better performance compared to that on CPU when the data size is 16 MB and the performance gain is proportional to the data size. We also show that the proposed system alleviates the utilization of CPU, and the overall performance of the database system is improved by offloading heavy encrypting and decrypting computation to GPU.

Key-value stores are widely used as storage backends, due to their simple, yet flexible interface for cache, storage, file system, and database systems. However, when used with high performance NVMe devices, their high compute requirements for data management often leave the device bandwidth under-utilized. This leads to a performance mismatch of what the device is capable of delivering and what it actually delivers, and the gains derived from high speed NVMe devices is nullified. In this paper, we introduce KV-SSD (Key-Value SSD) as a key technology in a holistic approach to overcome such performance imbalance. KV-SSD provides better scalability and performance by simplifying the software storage stack and consolidating redundancy, thereby lowering the overall CPU usage and releasing the memory to user applications. We evaluate the performance and scalability of KV-SSDs over state-of-the-art software alternatives built for traditional block SSDs. Our results show that, unlike traditional key-value systems, the overall performance ofKV-SSD scales linearly, and delivers 1.6 to 57x gains depending on the workload characteristics.

Modern hardware is abundantly parallel and increasingly heterogeneous. The numerous processing cores have non-uniform access latencies to the main memory and processor caches, which causes variability in the communication costs. Unfortunately, database systems mostly assume that all processing cores are the same and that microarchitecture differences are not significant enough to appear in critical database execution paths. As we demonstrate in this paper, however, non-uniform core topology does appear in the critical path and conventional database architectures achieve suboptimal and even worse, unpredictable performance. We perform a detailed performance analysis of OLTP deployments in servers with multiple cores per CPU (multicore) and multiple CPUs per server (multisocket). We compare different database deployment strategies where we vary the number and size of independent database instances running on a single server, from a single shared-everything instance to fine-grained shared-nothing configurations. We quantify the impact of non-uniform hardware on various deployments by (a) examining how efficiently each deployment uses the available hardware resources and (b) measuring the impact of distributed transactions and skewed requests on different workloads. We show that no strategy is optimal for all cases and that the best choice depends on the combination of hardware topology and workload characteristics. Finally, we argue that transaction processing systems must be aware of the hardware topology in order to achieve predictably high performance.

The same application implemented on different systems will necessarily present different workloads to the systems. Characterizations of workloads intended to represent the same application, but input to models of different systems, must also differ in analogous ways. We present a hierarchical method for characterizing a workload at increasing levels of detail such that every characterization at a lower level still accurately represents the workload at higher levels. We discuss our experience in using the method to feed the same application through a workload characterization “pipeline” to two different models of two different systems, a conventional relational database system and a logic-based distributed database system. We have developed programs that partially automate the characterization changes that are required when the system to be modeled changes.

Decision Support Systems (DSS) are computer programs that aid users in a problem solving or decision-making environment. DSS contains decision making information and other related data that may be of interest to unscrupulous competitors and stakeholders. Therefore, a DSS that is developed for web-based environment has to be secured from unauthorized access or malicious attacks. This study describes a framework of a secured DSS suitable to support decision making activities. Security issues are discussed from various perspectives with the proposed mechanisms to ensure a secured DSS implementation in distributed environment. Issues in web-based application development that include data growth, system performance and appearance compatibility are also presented together with their proposed solutions. Considering security and development issues could contribute towards successful implementation in the decision making process.

As one of most important characteristics of Web-based systems'workloads, burstiness is gaining more and more attentions. And synthetically generating bursty workloads is a key technique for performance analysis. In this paper, a configurable and intelligible synthetic bursty workload generation method for Web 2.0 benchmark Olio has been proposed based on 2-state Markovian arrival processes (MAP2). By comparing the actual value of index of dispersion for counts (IDC) estimated from system logs with the target value deduced from MAP2 model, we show that our method is more accurate than related work.

A methodology (JAGATH) and a tool based on it for establishing the performance of a distributed system within an empirical framework are presented. A set of performance variables representing the system performance is derived in terms of a combination of a set of performance measures. These performance measures represent the actual measurements of the events in the system. These performance variables are used to display the system performance status in the form of Kiviat graphs. The causal relationship between the performance variables and the internal system control variables and the workload characteristics can be established. This can be used in a performance ‘tuning’ system.

Database systems allow quick creation of performance problems. The goal of database systems is to allow the computer-illiterate to write complex and complete applications. It is the job of the syst...

Database systems are widely used in many fields. As the scale of database systems increases, more firms decide to deploy the database system in a distributed environment for the sake of data integrity fault tolerance. For guaranteeing the consistency of data, database management systems adopt isolation policies. This paper discusses probabilistic models for snapshot isolation of database management system. Snapshot isolation is an effective method to enhance the consistency of database system, although it degrades the system performance where the system has large network latency such as distributed database system. Also, under the failure-prone environment, a restart scheme is considered as one countermeasure. This paper proposes probabilistic models for the dynamics of snapshot isolation of database system and exhibits the optimization of system performance with respect to updating interval of snapshot isolation within the failure-prone environment from the analytical point of view. Numerical experiments are conducted to validate the effectiveness of analytical results by using real traffic data.

In this paper, we utilize a bandwidth-centric job communication model that captures the interaction and impact of simultaneously co-allocating jobs across multiple clusters. We make use of a parallel job model that seeks to capture both local and global communication access patterns. By doing so, we are able to explore scheduling strategies that attempt to improve average job turnaround time by selectively mapping jobs across cluster boundaries in a process known as job co-allocation. In this research, we focus on scheduling strategies that make use of available information such as network link utilization, per-processor bandwidths, and job communication topology in order to make intelligent decisions regarding application partition sizes and job placement. We provide results that help to establish the relationship between the quantity of information available a priori to the scheduler and its ability to improve overall system performance. Additionally, we demonstrate the dramatic impact that salient workload characteristics can have on the effectiveness of co-allocation

This paper studies cluster load balancing policies and system support for fine-grain network services. Load balancing on a cluster of machines has been studied extensively in the literature, mainly focusing on coarse-grain distributed computation. Fine-grain services introduce additional challenges because system states fluctuate rapidly,for those services and system performance is highly sensitive to various overhead. The main contribution of our work is to identify effective load balancing schemes for fine-grain services through simulations and empirical evaluations on synthetic workload and real traces. Another contribution is the design and implementation of a load balancing system in a Linux cluster that strikes a balance between acquiring enough load information and minimizing system overhead. Our study concludes that: 1) Random polling based load-balancing policies are well-suited for fine-grain network services: 2) A small poll size provides sufficient information for load balancing, while an excessively large poll size may in fact degrade the performance clue to polling overhead; 3) Discarding slow-responding polls can further improve system performance.