Autonomic Buffer Pool Configuration in PostgreSQL
|
|
- Irene Boyd
- 8 years ago
- Views:
Transcription
1 Autonomic Buffer Pool Configuration in PostgreSQL Wendy Powley, Pat Martin, Nailah Ogeer and Wenhu Tian School of Computing Queen s University Kingston, ON, Canada {wendy, martin, ogeer, tian}@cs.queensu.ca Abstract - As database management systems (DBMSs) continue to expand into new application areas, the complexity of the systems and the diversity of database workloads are increasing. Managing the performance of DBMSs via manual adjustment of resource allocations in this new environment has become impractical. Autonomic DBMSs shift the responsibility for performance management onto the systems themselves. This paper serves as a proof of concept, illustrating how autonomic principles can be applied to a DBMS to provide automatic sizing of buffer pools, a key resource in a DBMS. We describe an implementation of our autonomic system in PostgreSQL, an open source database management system, and provide a set of experiments that verify our approach. Keywords: Autonomic Computing, Database Management Systems, buffer pool configuration, performance, resource management. 1 Introduction As consumers demand more functionality and greater sophistication from Database Management Systems (DBMSs), vendors have been quick to deliver. However, the desire to manage complex data types, the ability to store very large objects, and the emergence of diverse and varying workloads are factors that have led to unmanageable complexity. It is no longer feasible for database administrators (DBAs) to manually configure and tune these systems. One approach to this management problem is an autonomic DBMS that is capable of automatically managing its resources to maintain acceptable performance in the face of changing conditions [3][6]. An autonomic DBMS must be able to perform typical configuration and tuning tasks including determining appropriate allocations for main memory areas such as the buffer pools and the sort heap, mapping database objects to buffer pools and adjusting the many DBMS configuration parameters to maintain acceptable performance. Effective use of the buffer area can greatly influence the performance of a DBMS by reducing the number of disk accesses performed by a transaction. Many DBMSs divide the buffer area into a number of independent buffer pools and database objects are allocated among the pools. Figure 1 illustrates this model where the indices, the stock table and the warehouse table are all assigned to separate, individual buffer pools, while the customer and item table share a buffer pool. To make effective use of the multiple buffer pools a DBA must choose an appropriate number of buffer pools, map database objects to buffer pools (we term this clustering), and accurately allocate the available memory among the buffer pools. These critical choices depend upon workload and system properties that may vary over time, perhaps requiring reconfiguration of the buffer pool settings. indices warehouse customer item stock Buffer Pools Figure 1: Multiple Buffer Pool Model In past research we have examined the issues related to buffer pool configuration and have proposed and implemented solutions for multiple buffer pool sizing [9][12] and for the clustering problem [14]. Our solutions, however, have been implemented as stand-alone tools, operating external to the DBMS code. These tools may assist a DBA in the decision making process, but they cannot, at this point, be considered part of a truly autonomic solution. It is our goal to augment a DBMS with our algorithms to provide fully autonomic buffer pool configuration, thus relieving a DBA of this responsibility. An autonomic DBMS system can be viewed as a feedback control loop as shown in Figure 2 [8], controlled by an Autonomic Manager. The autonomic manager oversees the monitoring of the DBMS (the Managed Element), and by analyzing the collected statistics in light of known policies and/or goals, it determines whether or not the performance is adequate. If necessary, a plan for reconfiguration is generated and executed. In this paper we present an implementation of autonomic functionality for the buffer pool size configuration problem using the open source database, PostgreSQL. Although this paper focuses on a specific task, the general framework presented is extensible to other problems and systems. The main contribution of this paper is a demonstration of the feasibility of our approach to adding autonomic features to a DBMS.
2 Monitor Figure 2. Feedback Control Loop The remainder of the paper is structured as follows. Section 2 presents related work. Section 3 describes our approach to automating the buffer pool size configuration problem and we present some experiments to validate our approach in Section 4. Section 5 provides a summary and suggests some ideas for future research. 2 Related Work Autonomic DBMSs, and autonomic systems in general, have received a great deal of attention both in the academic and the commercial worlds [3][6]. Self-tuning concepts have been applied to problems such as index selection [13], materialized view selection [1] and memory management [4][5][7][9]. Chaudhuri and Weikum [6] cite the need for self-tuning systems as an important reason to rethink current DBMS architectures. The buffer pool sizing problem has been tackled by several researchers. Dynamic Tuning [4] groups all database objects belonging to a transaction class into an individual buffer pool and assumes there is no data sharing among the transaction classes. To meet the specified transaction time goals, Dynamic Tuning tunes the buffer pool sizes according to the relationship the between buffer pool miss ratio and the response time of the transaction class. Data sharing was addressed by the Dynamic Reconfiguration algorithm [9]. This approach tunes the buffer pool sizes to satisfy the response time goals based on the assumption that the average response time of a transaction class is directly proportional to the average data access time of the transaction instance of that class, while the average data access time is a function of the buffer pool size. A potential problem with the goal-oriented approach is that it requires DBAs to pre-define reasonable classspecific goals, which can be a very difficult task. 3 Approach Autonomic Manager Analyze Knowledge Plan Managed Element Execute Our goal is to implement the buffer pool sizing approach proposed by Tian [12] within the autonomic framework shown in Figure 2. The sizing algorithm developed by Tian was originally implemented as a stand alone tool, external to the DBMS. The tool is used by a DBA when he/she suspects that a performance decrease may be due to incorrectly sized buffer pools. The DBA collects statistics that are used as input to the buffer pool sizing tool. The tool analyzes the statistics and suggests a new buffer pool size configuration which the DBA can use to manually reconfigure the buffer pools. Implementing these ideas within the autonomic framework, the DBMS must recognize that performance has degraded (monitoring) and that the buffer pools are no longer functioning efficiently (diagnosis/analysis). The system must automatically initiate the sizing algorithm (plan generation) and size the buffer pools accordingly (plan execution). This autonomic functionality is implemented within the DBMS itself. PostgreSQL is an open source DBMS and, for this reason, is an ideal candidate for demonstrating the incorporation of autonomic features. PostgreSQL, however, does not implement multiple buffer pools so our first task was to add multiple buffer pool functionality to the DBMS. Our modifications allow a DBA to specify the number of buffer pools and their initial sizes in the start up configuration file, and to assign database objects to buffer pools via the command line. Many objects may share a buffer pool. For the purpose of this paper we assume that the correct clustering of objects is known, and that the clustering solution remains stable throughout. 3.1 Monitoring To support the monitoring required for the sizing algorithm, additional code was added to the PostgreSQL statistics collector to include statistics for each buffer pool access including the number of logical reads, the number of physical reads, and the average data access time (DAT) incurred to fetch a data object. A logical read refers to any data request made by an application. The data may already be resident in the buffer pool or, if it is not, the request results in an access to disk to retrieve the data, thus referred to as a physical read. System monitoring incurs a certain amount of overhead, so it is important that monitoring is lightweight and that the monitoring facilities are used sparingly. For this reason, the statistics collector can be turned on and off as necessary. 3.2 Analysis/Diagnosis The analysis/diagnosis stage involves analyzing the performance data collected by the monitor to determine whether or not there has been a shift in performance and, if so, determining the possible cause(s). This is a complex, difficult task [11]. In the current approach, the system must determine if there has been a change in the efficiency of the buffer pool usage that may warrant a change to the buffer pool sizes. The standard metric for determining the efficiency of the buffer pools is the buffer pool hit rate, that is, the fraction of logical reads (all data accesses) satisfied by the buffer pool cache without requiring a physical read (a disk access). Maximizing the hit rate minimizes the number of physical data accesses, which in turn maximizes
3 throughput. We found, however, that hit rate is not necessarily the best choice for a cost function because all physical data accesses do not cost the same. The cost of a physical data access is influenced by several factors including the type of access (sequential or random), the physical device involved, where the data is placed on the device, and the load on the I/O system. We found that a more suitable criteria to evaluate the efficiency of the buffer pool is the average data access time (DAT), that is, the average time to satisfy a logical read request. This value is the average access time across all buffer pools. The DAT is dependent upon the buffer pool hit rate (the probability of finding the requested data in the buffer pool) and the types of accesses made to each buffer pool. An increase in the DAT indicates an increase in physical accesses given that physical I/O requires more time than accesses to RAM. To maximize buffer pool usage it is desirable to minimize the number of physical reads. The buffer pool sizing algorithm is triggered once a threshold of change in DAT is reached. The threshold in our system is set to 5 percent. Therefore, if the average DAT across buffer pools increases by 5 percent, the resizing algorithm is triggered. This threshold can be varied, but we found experimentally that a 5 percent change is enough to make a significant difference in the calculated optimal sizes [10]. 3.3 Plan Generation In the analysis/diagnosis stage the autonomic DBMS determines the potential cause of a performance shift. This may be a single resource, or a set of resources. In the plan generation stage, the system determines how the resource(s) should be tuned in order to correct the problem. In the current work, this involves determining how to resize the buffer pools to maximize performance. The buffer pool sizing algorithm is implemented as an internal routine in the DBMS. The task involves allocating the M buffer pages among the K buffer pools such that performance is maximized. This is, in general, a complex constrained optimization problem that cannot be solved exactly so heuristic methods must be used. We examined several methods and found that a greedy method is most effective for the problem [12]. The cost function used in our approach focuses on minimizing the average time for a logical data access, which takes into account both hit rate and physical data access cost. System throughput is inversely related to average logical access cost. We maintain data about buffer pool performance at different allocations for the given workload and use curve-fitting techniques to predict performance under new allocations. The overall system performance is maximized when the weighted cost of all logical reads (WcostLR) is minimized. The expected system cost of a logical read is calculated by averaging the cost of logical reads across all K buffer pools. WcostLR is expressed by: K WcostLR = ( Wi costlri) (1) i= 1 where W i is the buffer access weight on BP i and costlr i is the average cost of a logical read on BP i. The buffer access weight (W i ) indicates the percentage of the total number of logical reads that are serviced by BP i. A lower WcostLR indicates a lower data access time, thus yielding a faster transaction response time. The cost of memory access is obviously much faster than the cost of physical access, thus it is reasonable to assume that the data access time for a buffer pool is primarily the cost of disk accesses. Therefore, we have costlri nolri = costpri nopri (2) where nolr i and nopr i represent the number of logical reads and number of physical reads on BP i respectively, and costpr i indicates the cost of a physical read on BP i. From this equation, we obtain: nopri costlr i = costpri (3) nolri nopri Noting that defines the buffer pool miss ratio, nolri which can also be represented by (1 HR i ) where HR i is the hit ratio for BP i, we derive the following equation: costlri = costpri (1- HR i) (4) For a buffer pool that caches specific database objects, we assume that the cost to perform a physical read is fixed, namely, costpr i is a constant for a given buffer pool. Therefore, equation 4 theoretically represents a linear relationship between the buffer pool hit ratio and the buffer pool costlr i of the form: f(x) = kx + c (5) where the slope k is -costlr i, the intercept, c, is costpr i and x is HR i. To complete the equation, we use a curve-fitting technique to derive the parameters k and c. For a buffer pool BP i, two samplings at two different buffer pool size configurations (S 1 and S 2 ) are taken. We then have: costlr(s1) - costlr(s2) k = (6) HR(S1) - HR(S2) c = costlr(s1 ) - k HR(S1) There are many proposed approaches to predicting buffer hit rate in the literature. To simplify our implementation, we chose to use Belady s equation [2]. A greedy algorithm is then applied to search the optimal sizes for the buffer pools. The goal of the greedy algorithm is to minimize WcostLR. It starts from an initial size allocation and then examines all adjacent allocations that can be produced by shifting a fixed number of pages, say δ, between pairs of buffer pools. If the adjacent allocation with the lowest estimated WcostLR has a lower WcostLR than the current allocation then the sizing
4 algorithm moves to the adjacent state with the lowest cost. The algorithm halts when no further moves to new allocations can be made. 3.4 Plan Execution/Reconfiguration. The sizing algorithm described above produces a size configuration <S 1, S 2.. S K >, where K is the number of buffer pools, that minimizes the overall cost of a logical read. The autonomic DBMS dynamically resizes the buffer pools to the new size configuration. Each buffer pool in our modified version of PostgreSQL consists of a circular doubly linked list of buffer pages called a free list. Any page on the free list is a candidate for page replacement. If a buffer is in use by the DBMS it is considered to be pinned and thus unavailable for replacement. PostgreSQL uses a least recently used (LRU) page replacement algorithm. The buffer manager ensures that the LRU page is always found at the head of the list. Dirty pages (that is, pages that have been updated by the DBMS) are written to disk prior to replacement. Figure 4: Buffer Pools After Resizing (S BP1, S BP2 ) = (6, 10) 4 Validation A set of experiments was performed with the following objectives: Verify that the PostgreSQL multiple buffer pool implementation has a positive impact upon performance. Quantify the improvements associated with the dynamic sizing algorithm. Determine the additional overhead incurred by the monitoring required for the sizing algorithm. Determine the overhead involved in running the sizing algorithm and resizing the buffer pools. Figure 3: Contents of 2 Buffer Pools (S BP1, S BP2 ) = (8, 8) Figure 3 shows the contents of 2 buffer pools with a size configuration of (S BP1, S BP2 ) = (8, 8). Buffers 1, 2, 6, 13, 14 and 15 are currently in use by the DBMS. If the buffer pool sizing algorithm suggests a new size configuration of (S BP1, S BP2 ) = (6, 10) then buffers 6 and 7 are shifted to the second buffer pool as shown in Figure 4. Reassigning buffer 7 to a new buffer pool simply involves resetting the next and previous pointers in the lists. Buffer 6, however, is in use and cannot be shifted immediately. This buffer remains associated with Buffer Pool 1 until after the next access, at which point, the contents, if dirty are flushed to disk and the buffer descriptor changed to associate buffer 6 with Buffer Pool 2. Since the data has been accessed, this buffer appears as a pinned buffer in Buffer Pool 2. Illustrate our dynamic approach to autonomic buffer pool sizing. The experimental results are presented briefly here. Full details of these experiments, as well as additional experiments, can be found in [10]. Our experiments were run using a modified version of PostgreSQL (7.3.2) (as well as the original version) on a Sun Solaris machine configured with 15 9GB disks, 2GB of RAM and 6 processors. We use a database with two tables: TabA (15000 tuples), and TabB (7500 tuples). Each table has unique id field (1 to N, with N being the number of tuples in the table). Each tuple is 50 bytes. 4.1 Workload The following two transactions make up our workload: Transaction A: select * from TabA; select id from TabA where id < 50; select id from TabA where id < 100; Transaction B: select * fromtabb; select id from TabB where id < 50; select id from TabB where id < 100;
5 Each transaction requires a table scan of a large table, followed by a selection of a portion (or hot set ) of the data contained in the table. If the buffer pool is too small to hold the entire table, many pages will be swapped in and out of the buffer pool. If the two tables share a buffer pool that is not large enough to accommodate both tables, the execution of Transaction B will replace pages occupied by TabA and the execution of Transaction A will replace pages occupied by TabB, resulting in an excess of physical I/O. The capacity of each buffer in PostgreSQL is 8 kilobytes. Therefore, the number of tuples that can fit in a buffer pool with K pages is: Number of tuples = floor[ (K * 8192) / 50 ] (7) To hold the contents of TabA and TabB we require a total of 138 buffers (92 for TabA and 46 for TabB). To illustrate how our autonomic sizing system works we restrict our entire buffer area to 124 buffers, split between 2 buffer pools, B1 and B2. TabA is assigned to B1 while TabB is assigned to B2. To illustrate the effectiveness of the multiple buffer pool implementation, we use a buffer pool size configuration of (S B1, S B2 ) = (64, 64), an equal distribution of buffers between the two buffer pools. In this case we expect to see a hit rate of near 0 for BP1 and a hit rate of 100 percent for BP2 (since BP2 is large enough to hold the entire contents of TabB), which is what we observe. We compare the throughput (transactions per second) using the modified version of PostgreSQL to the original, unmodified version of PostgreSQL. In both cases the statistics monitor is turned on for the duration. We observe an improvement in throughput of 7.6 percent with the multiple buffer pool configuration, verifying that by segregating the tables, overall system performance has improved significantly. Monitoring incurs a certain amount of overhead as statistics must be collected for every buffer access for each buffer pool. To examine the impact of the monitoring, we compare the performance of our workload using the modified version of PostgreSQL with the statistics collector off, then with the statistics collector on. With 5 clients issuing transactions simultaneously, we observe a 16 percent decrease in throughput when the statistics collector is turned on. This significant overhead suggests that the current statistics monitor must be invoked sparingly. The main goal of this research is to integrate autonomic buffer pool sizing to PostgreSQL to illustrate that the feedback approach is feasible and beneficial. The system must monitor the performance, recognize that the buffer pools should be resized, run the sizing algorithm, and dynamically resize the buffer pools to improve performance. The following sequence of events illustrates one scenario that we used to demonstrate our approach. Note that all steps are dynamic, and that the workload described above is running continuously with 5 clients issuing transactions simultaneously. 1. An initial optimal size configuration was determined by collecting statistics under 2 buffer pool size configurations, (S B1, S B2 ) = (64, 64) and (S B1, S B2 ) = (96, 32). These statistics were used by the buffer pool sizing algorithm to determine the optimal size configuration, (S B1, S B2 ) = (81, 47). 2. The workload was executed under the optimal buffer pool size configuration, (S B1, S B2 ) = (81, 47) as determined in Step 1. Under this configuration, after 800 transactions, the data access time (DAT) was recorded as (DAT BP1, DAT BP2 ) = (112.8, 0) where DAT is measured in milliseconds. 3. The sizes of the tables TabA and TabB were modified to simulate a change in the workload tuples were removed from TabA and 7500 tuples were added to TabB. The DAT after the database modifications, running the same workload, was measured at (DAT BP1, DAT BP2 ) = (0, 121). Given that this average was more than 5 percent greater than the average DAT measured earlier, (DAT BP1, DAT BP2 ) = (112.8, 0), the resizing algorithm was triggered. The algorithm used the current statistics combined with the statistics from previous buffer pool configuration to suggest a new size configuration, (S B1, S B2 ) = (55, 73). The DAT measured under this configuration was (DAT BP1, DAT BP2 ) = (0, 71.5). This scenario illustrates that our autonomic DBMS can recognize a change in performance and respond appropriately, and successfully, to this change. The CPU overhead was measured during the execution of the sizing algorithm and during the buffer pool resizing. CPU usage increased approximately 1.5 percent during this time, indicating that the sizing algorithm and the buffer pool resizing incurs only negligible overhead. 5 Summary We have presented an approach to introducing autonomic features into a database management system to automate the complex management issues associated with these systems. We have demonstrated the feasibility of our approach using the buffer pool sizing problem as an example. The system is able to monitor itself, recognize that the buffer pools are functioning less efficiently and correct the problem dynamically. The main downfall of the approach is the overhead of monitoring. An autonomic system must be self-aware. For a system to know itself, it must be able to monitor its own performance and be able to compare current and past performance to recognize changes. Future work will focus on lightweight and less obtrusive monitoring techniques.
6 We have incorporated Xu s buffer pool clustering algorithm [12] into PostgreSQL and we are in the process of testing this approach in conjunction with the sizing algorithm. The main issue with this implementation is determining when it is necessary to re-cluster the database objects. This involves recognizing that the workload has changed significantly and/or the database size has changed significantly, thus affecting the way in which the objects are accessed. References [1] S. Agrawal, S. Chaudhuri and V. Narasayya. Automated Selection of Materialized Views and Indexes, Proc. of 26 th Int. Conf. on Very Large Databases, Cairo, Egypt, September [2] L. A. Belady, A Study of Replacement Algorithms for Virtual Storage Computer, IBM System Journal, Vol 5 No. 2, pp , July [11] S. Parekh, N. Gandhi, J. Hellerstein et al, Using Control Theory to Achieve Service Level Objectives In Performance Management, Real-Time Systems, Vol. 23, No. 1-2, pp , [12] W. Tian, W. Powley and P. Martin. Techniques for Automatically Sizing Multiple Buffer Pools in DB2, Proceedings of CASCON 2003, Toronto, Canada, pp , [13] G. Valentin, M. Zuliani, D. Zilio, G. Lohman and A. Skelly. DB2 Advisor: An Optimizer Smart Enough to Recommend Its Own Indexes, Proceedings of Int. Conf. on Data Engineering, San Diego, California, pp , February [14] X. Xu, P. Martin and W. Powley, Configuring Buffer Pools in DB2/UDB, Proceedings of CASCON 2002, Toronto, Canada, pp , Oct [3] P. Bernstein, M. Brodie and S.Ceri, et al., The Asilomar Report on Database Research, ACM SIGMOD Record, Vol 27, No. 4, pp , Dec [4] K. Brown, M. Carey and M. Livny, Managing Memory to Meet Multiclass Workload Response Time Goals, Proc. Of 19th Int. Conf. on Very Large Databases, Dublin, Ireland, pp , Aug [5] K. Brown, M. Carey and M. Livny, Goal Oriented Buffer Management Revisited, ACM SIGMOD Record, Vol 25 No. 2, pp , June [6] S. Chaudhuri, G. Weikum, Rethinking Database System Architecture: Towards a Self-Tuning RISC-Style Database System, Proc. of 26th Int. Conf. on Very Large Databases, Cairo, Egypt, pp 1-10, Sept [7] J. Y. Chung, D. Ferguson and G. Wang, Goal Oriented Dynamic Buffer Pool Management for Database Systems, Proc. of Int. Conf. on Engineering of Complex Systems (ICECCS 95), Ft. Lauderdale, Florida, Nov [8] Kephart, J.O., Chess, D.M., The Vision of Autonomic Computing, Computer, Vol 36 No. 1, pp , [9] P. Martin, H. Li, M. Zheng, K. Romanufa and W. Powley, Dynamic Reconfiguration Algorithm: Dynamically Tuning Buffer Pools, Proc. of 11th Int. Conf. on Database and Expert Systems Applications, London, UK, pp , Sept [10] N. Ogeer, "Buffer Management Strategies for PostgreSQL," MSc Thesis, Queen s University, April 2004.
Managing Database Server Performance to Meet QoS Requirements in Electronic Commerce Systems *
Managing Database Server Performance to Meet QoS Requirements in Electronic Commerce Systems * Abstract Patrick Martin 1, Wendy Powley 1, Hoi-Ying Li 1 and Keri Romanufa 2 1 Department of Computing and
More informationAbstract. Workload Class Importance Policy in Autonomic Database Management Systems
Workload Class Importance Policy in Autonomic Database Management Systems Harley Boughton Queen s University Kingston, ON, Canada harley@cs.queensu.ca Pat Martin Queen s University Kingston, ON, Canada
More informationDatabase Performance Monitoring and Tuning Using Intelligent Agent Assistants
Database Performance Monitoring and Tuning Using Intelligent Agent Assistants Sherif Elfayoumy and Jigisha Patel School of Computing, University of North Florida, Jacksonville, FL,USA Abstract - Fast databases
More informationDatabase Virtualization: A New Frontier for Database Tuning and Physical Design
Database Virtualization: A New Frontier for Database Tuning and Physical Design Ahmed A. Soror Ashraf Aboulnaga Kenneth Salem David R. Cheriton School of Computer Science University of Waterloo {aakssoro,
More informationA Capacity Planning Study of Database Management Systems with OLAP Workloads
A Capacity Planning Study of Database Management Systems with OLAP Workloads By XILIN CUI A thesis submitted to the School of Computing in conformity with the requirements for the Degree of the Master
More informationAutomatic Diagnosis of Performance Problems in Database Management Systems
Automatic Diagnosis of Performance Problems in Database Management Systems by Darcy G. Benoit A thesis submitted to the School of Computing in conformity with the requirements for the degree of Doctor
More informationAUTONOMIC COMPUTING IN SQL SERVER
Seventh IEEE/ACIS International Conference on Computer and Information Science AUTONOMIC COMPUTING IN SQL SERVER Abdul Mateen, Basit Raza, Tauqeer Hussain University of Central Punjab, Lahore, Pakistan
More informationStorage in Database Systems. CMPSCI 445 Fall 2010
Storage in Database Systems CMPSCI 445 Fall 2010 1 Storage Topics Architecture and Overview Disks Buffer management Files of records 2 DBMS Architecture Query Parser Query Rewriter Query Optimizer Query
More informationVirtuoso and Database Scalability
Virtuoso and Database Scalability By Orri Erling Table of Contents Abstract Metrics Results Transaction Throughput Initializing 40 warehouses Serial Read Test Conditions Analysis Working Set Effect of
More informationArchitecture for an Autonomic Web Services Environment
Architecture for an Autonomic Web Services Environment Wenhu Tian, Farhana Zulkernine, Jared Zebedee, Wendy Powley, Pat Martin School of Computing, Queen s University, Kingston, ON Canada {tian, farhana,
More informationA Framework for Automated Database TuningUsing Dynamic SGA Parameters and Basic Operating System Utilities
Database Systems Journal vol. III, no. 4/2012 25 A Framework for Automated Database TuningUsing Dynamic SGA Parameters and Basic Operating System Utilities Hitesh KUMAR SHARMA1, Aditya SHASTRI2, Ranjit
More informationPART IV Performance oriented design, Performance testing, Performance tuning & Performance solutions. Outline. Performance oriented design
PART IV Performance oriented design, Performance testing, Performance tuning & Performance solutions Slide 1 Outline Principles for performance oriented design Performance testing Performance tuning General
More informationWindows Server 2008 R2 Hyper-V Live Migration
Windows Server 2008 R2 Hyper-V Live Migration Table of Contents Overview of Windows Server 2008 R2 Hyper-V Features... 3 Dynamic VM storage... 3 Enhanced Processor Support... 3 Enhanced Networking Support...
More informationUsing Synology SSD Technology to Enhance System Performance Synology Inc.
Using Synology SSD Technology to Enhance System Performance Synology Inc. Synology_SSD_Cache_WP_ 20140512 Table of Contents Chapter 1: Enterprise Challenges and SSD Cache as Solution Enterprise Challenges...
More informationEnergy-aware Memory Management through Database Buffer Control
Energy-aware Memory Management through Database Buffer Control Chang S. Bae, Tayeb Jamel Northwestern Univ. Intel Corporation Presented by Chang S. Bae Goal and motivation Energy-aware memory management
More informationAutomatic Virtual Machine Configuration for Database Workloads
Automatic Virtual Machine Configuration for Database Workloads Ahmed A. Soror Umar Farooq Minhas Ashraf Aboulnaga Kenneth Salem Peter Kokosielis Sunil Kamath University of Waterloo IBM Toronto Lab {aakssoro,
More informationIBM DB2: LUW Performance Tuning and Monitoring for Single and Multiple Partition DBs
coursemonster.com/au IBM DB2: LUW Performance Tuning and Monitoring for Single and Multiple Partition DBs View training dates» Overview Learn how to tune for optimum performance the IBM DB2 9 for Linux,
More informationIBM Software Information Management. Scaling strategies for mission-critical discovery and navigation applications
IBM Software Information Management Scaling strategies for mission-critical discovery and navigation applications Scaling strategies for mission-critical discovery and navigation applications Contents
More informationOptimal Service Pricing for a Cloud Cache
Optimal Service Pricing for a Cloud Cache K.SRAVANTHI Department of Computer Science & Engineering (M.Tech.) Sindura College of Engineering and Technology Ramagundam,Telangana G.LAKSHMI Asst. Professor,
More informationInge Os Sales Consulting Manager Oracle Norway
Inge Os Sales Consulting Manager Oracle Norway Agenda Oracle Fusion Middelware Oracle Database 11GR2 Oracle Database Machine Oracle & Sun Agenda Oracle Fusion Middelware Oracle Database 11GR2 Oracle Database
More informationOracle Database 12c: Performance Management and Tuning NEW
Oracle University Contact Us: 1.800.529.0165 Oracle Database 12c: Performance Management and Tuning NEW Duration: 5 Days What you will learn In the Oracle Database 12c: Performance Management and Tuning
More informationPerformance Modeling and Analysis of a Database Server with Write-Heavy Workload
Performance Modeling and Analysis of a Database Server with Write-Heavy Workload Manfred Dellkrantz, Maria Kihl 2, and Anders Robertsson Department of Automatic Control, Lund University 2 Department of
More informationSelf-Tuning Memory Management of A Database System
Self-Tuning Memory Management of A Database System Yixin Diao diao@us.ibm.com IM 2009 Tutorial: Recent Advances in the Application of Control Theory to Network and Service Management DB2 Self-Tuning Memory
More informationIBM Tivoli Monitoring for Databases
Enhance the availability and performance of database servers IBM Tivoli Monitoring for Databases Highlights Integrated, intelligent database monitoring for your on demand business Preconfiguration of metric
More informationRackspace Cloud Databases and Container-based Virtualization
Rackspace Cloud Databases and Container-based Virtualization August 2012 J.R. Arredondo @jrarredondo Page 1 of 6 INTRODUCTION When Rackspace set out to build the Cloud Databases product, we asked many
More informationUsing Synology SSD Technology to Enhance System Performance. Based on DSM 5.2
Using Synology SSD Technology to Enhance System Performance Based on DSM 5.2 Table of Contents Chapter 1: Enterprise Challenges and SSD Cache as Solution Enterprise Challenges... 3 SSD Cache as Solution...
More informationVirtualization Technology using Virtual Machines for Cloud Computing
International OPEN ACCESS Journal Of Modern Engineering Research (IJMER) Virtualization Technology using Virtual Machines for Cloud Computing T. Kamalakar Raju 1, A. Lavanya 2, Dr. M. Rajanikanth 2 1,
More informationDB2 for Linux, UNIX, and Windows Performance Tuning and Monitoring Workshop
DB2 for Linux, UNIX, and Windows Performance Tuning and Monitoring Workshop Duration: 4 Days What you will learn Learn how to tune for optimum performance the IBM DB2 9 for Linux, UNIX, and Windows relational
More informationWindows Server 2008 R2 Hyper-V Live Migration
Windows Server 2008 R2 Hyper-V Live Migration White Paper Published: August 09 This is a preliminary document and may be changed substantially prior to final commercial release of the software described
More informationAdvanced Load Balancing Mechanism on Mixed Batch and Transactional Workloads
Advanced Load Balancing Mechanism on Mixed Batch and Transactional Workloads G. Suganthi (Member, IEEE), K. N. Vimal Shankar, Department of Computer Science and Engineering, V.S.B. Engineering College,
More informationDatabases Going Virtual? Identifying the Best Database Servers for Virtualization
Identifying the Best Database Servers for Virtualization By Confio Software Confio Software 4772 Walnut Street, Suite 100 Boulder, CO 80301 www.confio.com Many companies are turning to virtualization in
More informationLoad Distribution in Large Scale Network Monitoring Infrastructures
Load Distribution in Large Scale Network Monitoring Infrastructures Josep Sanjuàs-Cuxart, Pere Barlet-Ros, Gianluca Iannaccone, and Josep Solé-Pareta Universitat Politècnica de Catalunya (UPC) {jsanjuas,pbarlet,pareta}@ac.upc.edu
More informationOperating Systems, 6 th ed. Test Bank Chapter 7
True / False Questions: Chapter 7 Memory Management 1. T / F In a multiprogramming system, main memory is divided into multiple sections: one for the operating system (resident monitor, kernel) and one
More informationPERFORMANCE EVALUATION OF DATABASE MANAGEMENT SYSTEMS BY THE ANALYSIS OF DBMS TIME AND CAPACITY
Vol.2, Issue.2, Mar-Apr 2012 pp-067-072 ISSN: 2249-6645 PERFORMANCE EVALUATION OF DATABASE MANAGEMENT SYSTEMS BY THE ANALYSIS OF DBMS TIME AND CAPACITY Aparna Kaladi 1 and Priya Ponnusamy 2 1 M.E Computer
More informationBridgeWays Management Pack for VMware ESX
Bridgeways White Paper: Management Pack for VMware ESX BridgeWays Management Pack for VMware ESX Ensuring smooth virtual operations while maximizing your ROI. Published: July 2009 For the latest information,
More informationMass Storage Structure
Mass Storage Structure 12 CHAPTER Practice Exercises 12.1 The accelerating seek described in Exercise 12.3 is typical of hard-disk drives. By contrast, floppy disks (and many hard disks manufactured before
More informationSAP HANA PLATFORM Top Ten Questions for Choosing In-Memory Databases. Start Here
PLATFORM Top Ten Questions for Choosing In-Memory Databases Start Here PLATFORM Top Ten Questions for Choosing In-Memory Databases. Are my applications accelerated without manual intervention and tuning?.
More informationPerformance And Scalability In Oracle9i And SQL Server 2000
Performance And Scalability In Oracle9i And SQL Server 2000 Presented By : Phathisile Sibanda Supervisor : John Ebden 1 Presentation Overview Project Objectives Motivation -Why performance & Scalability
More informationA Survey of Shared File Systems
Technical Paper A Survey of Shared File Systems Determining the Best Choice for your Distributed Applications A Survey of Shared File Systems A Survey of Shared File Systems Table of Contents Introduction...
More informationThis is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 12902
Open Archive TOULOUSE Archive Ouverte (OATAO) OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited
More informationAvoiding Performance Bottlenecks in Hyper-V
Avoiding Performance Bottlenecks in Hyper-V Identify and eliminate capacity related performance bottlenecks in Hyper-V while placing new VMs for optimal density and performance Whitepaper by Chris Chesley
More informationIndex Selection Techniques in Data Warehouse Systems
Index Selection Techniques in Data Warehouse Systems Aliaksei Holubeu as a part of a Seminar Databases and Data Warehouses. Implementation and usage. Konstanz, June 3, 2005 2 Contents 1 DATA WAREHOUSES
More informationEFFICIENT EXTERNAL SORTING ON FLASH MEMORY EMBEDDED DEVICES
ABSTRACT EFFICIENT EXTERNAL SORTING ON FLASH MEMORY EMBEDDED DEVICES Tyler Cossentine and Ramon Lawrence Department of Computer Science, University of British Columbia Okanagan Kelowna, BC, Canada tcossentine@gmail.com
More informationARH_Db_Tuner: The GUI tool to Monitor and Diagnose the SGA Parameters Automatically
Database Systems Journal vol. IV, no. 1/2013 3 ARH_Db_Tuner: The GUI tool to Monitor and Diagnose the SGA Parameters Automatically Hitesh KUMAR SHARMA 1, Aditya SHASTRI 2, Ranjit BISWAS 3 1 Assistant Professor,
More informationOracle Rdb Performance Management Guide
Oracle Rdb Performance Management Guide Solving the Five Most Common Problems with Rdb Application Performance and Availability White Paper ALI Database Consultants 803-648-5931 www.aliconsultants.com
More informationBenchmarking Hadoop & HBase on Violin
Technical White Paper Report Technical Report Benchmarking Hadoop & HBase on Violin Harnessing Big Data Analytics at the Speed of Memory Version 1.0 Abstract The purpose of benchmarking is to show advantages
More informationA Framework for Automatic Performance Monitoring, Analysis and Optimisation of Component Based Software Systems
A Framework for Automatic Performance Monitoring, Analysis and Optimisation of Component Based Software Systems Ada Diaconescu *, John Murphy ** Performance Engineering Laboratory Dublin City University,
More informationSecondary Storage. Any modern computer system will incorporate (at least) two levels of storage: magnetic disk/optical devices/tape systems
1 Any modern computer system will incorporate (at least) two levels of storage: primary storage: typical capacity cost per MB $3. typical access time burst transfer rate?? secondary storage: typical capacity
More informationSQL Memory Management in Oracle9i
SQL Management in Oracle9i Benoît Dageville Mohamed Zait Oracle Corporation Oracle Corporation 500 Oracle Parway 500 Oracle Parway Redwood Shores, CA 94065 Redwood Shores, CA 94065 U.S.A U.S.A Benoit.Dageville@oracle.com
More informationThe Benefits of Virtualizing
T E C H N I C A L B R I E F The Benefits of Virtualizing Aciduisismodo Microsoft SQL Dolore Server Eolore in Dionseq Hitachi Storage Uatummy Environments Odolorem Vel Leveraging Microsoft Hyper-V By Heidi
More informationPERFORMANCE TUNING IN MICROSOFT SQL SERVER DBMS
Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 4, Issue. 6, June 2015, pg.381
More informationMonitoring Databases on VMware
Monitoring Databases on VMware Ensure Optimum Performance with the Correct Metrics By Dean Richards, Manager, Sales Engineering Confio Software 4772 Walnut Street, Suite 100 Boulder, CO 80301 www.confio.com
More information6. Storage and File Structures
ECS-165A WQ 11 110 6. Storage and File Structures Goals Understand the basic concepts underlying different storage media, buffer management, files structures, and organization of records in files. Contents
More informationEvaluation Report: Accelerating SQL Server Database Performance with the Lenovo Storage S3200 SAN Array
Evaluation Report: Accelerating SQL Server Database Performance with the Lenovo Storage S3200 SAN Array Evaluation report prepared under contract with Lenovo Executive Summary Even with the price of flash
More informationVirtualCenter Database Performance for Microsoft SQL Server 2005 VirtualCenter 2.5
Performance Study VirtualCenter Database Performance for Microsoft SQL Server 2005 VirtualCenter 2.5 VMware VirtualCenter uses a database to store metadata on the state of a VMware Infrastructure environment.
More informationAn Oracle White Paper November 2010. Oracle Real Application Clusters One Node: The Always On Single-Instance Database
An Oracle White Paper November 2010 Oracle Real Application Clusters One Node: The Always On Single-Instance Database Executive Summary... 1 Oracle Real Application Clusters One Node Overview... 1 Always
More informationNetwork Performance Monitoring at Small Time Scales
Network Performance Monitoring at Small Time Scales Konstantina Papagiannaki, Rene Cruz, Christophe Diot Sprint ATL Burlingame, CA dina@sprintlabs.com Electrical and Computer Engineering Department University
More informationReconfigurable Architecture Requirements for Co-Designed Virtual Machines
Reconfigurable Architecture Requirements for Co-Designed Virtual Machines Kenneth B. Kent University of New Brunswick Faculty of Computer Science Fredericton, New Brunswick, Canada ken@unb.ca Micaela Serra
More informationChapter 1 Computer System Overview
Operating Systems: Internals and Design Principles Chapter 1 Computer System Overview Eighth Edition By William Stallings Operating System Exploits the hardware resources of one or more processors Provides
More informationScheduling Allowance Adaptability in Load Balancing technique for Distributed Systems
Scheduling Allowance Adaptability in Load Balancing technique for Distributed Systems G.Rajina #1, P.Nagaraju #2 #1 M.Tech, Computer Science Engineering, TallaPadmavathi Engineering College, Warangal,
More informationConcept of Cache in web proxies
Concept of Cache in web proxies Chan Kit Wai and Somasundaram Meiyappan 1. Introduction Caching is an effective performance enhancing technique that has been used in computer systems for decades. However,
More informationUnderstanding Data Locality in VMware Virtual SAN
Understanding Data Locality in VMware Virtual SAN July 2014 Edition T E C H N I C A L M A R K E T I N G D O C U M E N T A T I O N Table of Contents Introduction... 2 Virtual SAN Design Goals... 3 Data
More informationDB2 LUW Performance Tuning and Monitoring for Single and Multiple Partition DBs
Kod szkolenia: Tytuł szkolenia: CL442PL DB2 LUW Performance Tuning and Monitoring for Single and Multiple Partition DBs Dni: 5 Opis: Learn how to tune for optimum the IBM DB2 9 for Linux, UNIX, and Windows
More informationOverview of Storage and Indexing. Data on External Storage. Alternative File Organizations. Chapter 8
Overview of Storage and Indexing Chapter 8 How index-learning turns no student pale Yet holds the eel of science by the tail. -- Alexander Pope (1688-1744) Database Management Systems 3ed, R. Ramakrishnan
More informationBest Practices for Optimizing SQL Server Database Performance with the LSI WarpDrive Acceleration Card
Best Practices for Optimizing SQL Server Database Performance with the LSI WarpDrive Acceleration Card Version 1.0 April 2011 DB15-000761-00 Revision History Version and Date Version 1.0, April 2011 Initial
More informationWHITE PAPER. SQL Server License Reduction with PernixData FVP Software
WHITE PAPER SQL Server License Reduction with PernixData FVP Software 1 Beyond Database Acceleration Poor storage performance continues to be the largest pain point with enterprise Database Administrators
More informationBRINGING INFORMATION RETRIEVAL BACK TO DATABASE MANAGEMENT SYSTEMS
BRINGING INFORMATION RETRIEVAL BACK TO DATABASE MANAGEMENT SYSTEMS Khaled Nagi Dept. of Computer and Systems Engineering, Faculty of Engineering, Alexandria University, Egypt. khaled.nagi@eng.alex.edu.eg
More informationApplication of Predictive Analytics for Better Alignment of Business and IT
Application of Predictive Analytics for Better Alignment of Business and IT Boris Zibitsker, PhD bzibitsker@beznext.com July 25, 2014 Big Data Summit - Riga, Latvia About the Presenter Boris Zibitsker
More information89 Fifth Avenue, 7th Floor. New York, NY 10003. www.theedison.com 212.367.7400. White Paper. HP 3PAR Adaptive Flash Cache: A Competitive Comparison
89 Fifth Avenue, 7th Floor New York, NY 10003 www.theedison.com 212.367.7400 White Paper HP 3PAR Adaptive Flash Cache: A Competitive Comparison Printed in the United States of America Copyright 2014 Edison
More informationOverview of Storage and Indexing
Overview of Storage and Indexing Chapter 8 How index-learning turns no student pale Yet holds the eel of science by the tail. -- Alexander Pope (1688-1744) Database Management Systems 3ed, R. Ramakrishnan
More informationPostgres Plus Advanced Server
Postgres Plus Advanced Server An Updated Performance Benchmark An EnterpriseDB White Paper For DBAs, Application Developers & Enterprise Architects June 2013 Table of Contents Executive Summary...3 Benchmark
More informationFigure 1. The cloud scales: Amazon EC2 growth [2].
- Chung-Cheng Li and Kuochen Wang Department of Computer Science National Chiao Tung University Hsinchu, Taiwan 300 shinji10343@hotmail.com, kwang@cs.nctu.edu.tw Abstract One of the most important issues
More informationStorage Class Memory Aware Data Management
Storage Class Memory Aware Data Management Bishwaranjan Bhattacharjee IBM T. J. Watson Research Center bhatta@us.ibm.com George A. Mihaila IBM T. J. Watson Research Center mihaila@us.ibm.com Mustafa Canim
More informationDemystifying Deduplication for Backup with the Dell DR4000
Demystifying Deduplication for Backup with the Dell DR4000 This Dell Technical White Paper explains how deduplication with the DR4000 can help your organization save time, space, and money. John Bassett
More informationPostgreSQL Performance Characteristics on Joyent and Amazon EC2
OVERVIEW In today's big data world, high performance databases are not only required but are a major part of any critical business function. With the advent of mobile devices, users are consuming data
More informationA Novel Way of Deduplication Approach for Cloud Backup Services Using Block Index Caching Technique
A Novel Way of Deduplication Approach for Cloud Backup Services Using Block Index Caching Technique Jyoti Malhotra 1,Priya Ghyare 2 Associate Professor, Dept. of Information Technology, MIT College of
More informationReal Time Network Server Monitoring using Smartphone with Dynamic Load Balancing
www.ijcsi.org 227 Real Time Network Server Monitoring using Smartphone with Dynamic Load Balancing Dhuha Basheer Abdullah 1, Zeena Abdulgafar Thanoon 2, 1 Computer Science Department, Mosul University,
More informationA Business Driven Cloud Optimization Architecture
A Business Driven Cloud Optimization Architecture Marin Litoiu York University, Canada mlitoiu@yorku.ca Murray Woodside Carleton University, Canada Johnny Wong University of Waterloo, Canada Joanna Ng,
More information2. Research and Development on the Autonomic Operation. Control Infrastructure Technologies in the Cloud Computing Environment
R&D supporting future cloud computing infrastructure technologies Research and Development on Autonomic Operation Control Infrastructure Technologies in the Cloud Computing Environment DEMPO Hiroshi, KAMI
More informationMuse Server Sizing. 18 June 2012. Document Version 0.0.1.9 Muse 2.7.0.0
Muse Server Sizing 18 June 2012 Document Version 0.0.1.9 Muse 2.7.0.0 Notice No part of this publication may be reproduced stored in a retrieval system, or transmitted, in any form or by any means, without
More informationPARALLELS CLOUD SERVER
PARALLELS CLOUD SERVER An Introduction to Operating System Virtualization and Parallels Cloud Server 1 Table of Contents Introduction... 3 Hardware Virtualization... 3 Operating System Virtualization...
More informationAzure Scalability Prescriptive Architecture using the Enzo Multitenant Framework
Azure Scalability Prescriptive Architecture using the Enzo Multitenant Framework Many corporations and Independent Software Vendors considering cloud computing adoption face a similar challenge: how should
More informationOptimizing LTO Backup Performance
Optimizing LTO Backup Performance July 19, 2011 Written by: Ash McCarty Contributors: Cedrick Burton Bob Dawson Vang Nguyen Richard Snook Table of Contents 1.0 Introduction... 3 2.0 Host System Configuration...
More informationStorage I/O Control: Proportional Allocation of Shared Storage Resources
Storage I/O Control: Proportional Allocation of Shared Storage Resources Chethan Kumar Sr. Member of Technical Staff, R&D VMware, Inc. Outline The Problem Storage IO Control (SIOC) overview Technical Details
More informationQuantifying the Performance Degradation of IPv6 for TCP in Windows and Linux Networking
Quantifying the Performance Degradation of IPv6 for TCP in Windows and Linux Networking Burjiz Soorty School of Computing and Mathematical Sciences Auckland University of Technology Auckland, New Zealand
More informationOpenMosix Presented by Dr. Moshe Bar and MAASK [01]
OpenMosix Presented by Dr. Moshe Bar and MAASK [01] openmosix is a kernel extension for single-system image clustering. openmosix [24] is a tool for a Unix-like kernel, such as Linux, consisting of adaptive
More informationMicrosoft SQL Server OLTP Best Practice
Microsoft SQL Server OLTP Best Practice The document Introduction to Transactional (OLTP) Load Testing for all Databases provides a general overview on the HammerDB OLTP workload and the document Microsoft
More informationDatabase Design for Performance Data in Integrated Network Management System
Database Design for Performance Data in Integrated Network Management System YeonJoo Na, IlSoo Ahn Network Management Solutions Lab. Telecommunication Systems Division Samsung Electronics Co., Korea e-mail:
More informationUsing Synology SSD Technology to Enhance System Performance Synology Inc.
Using Synology SSD Technology to Enhance System Performance Synology Inc. Synology_WP_ 20121112 Table of Contents Chapter 1: Enterprise Challenges and SSD Cache as Solution Enterprise Challenges... 3 SSD
More informationLocality Based Protocol for MultiWriter Replication systems
Locality Based Protocol for MultiWriter Replication systems Lei Gao Department of Computer Science The University of Texas at Austin lgao@cs.utexas.edu One of the challenging problems in building replication
More informationPerformance Comparison of Fujitsu PRIMERGY and PRIMEPOWER Servers
WHITE PAPER FUJITSU PRIMERGY AND PRIMEPOWER SERVERS Performance Comparison of Fujitsu PRIMERGY and PRIMEPOWER Servers CHALLENGE Replace a Fujitsu PRIMEPOWER 2500 partition with a lower cost solution that
More informationImpact of Control Theory on QoS Adaptation in Distributed Middleware Systems
Impact of Control Theory on QoS Adaptation in Distributed Middleware Systems Baochun Li Electrical and Computer Engineering University of Toronto bli@eecg.toronto.edu Klara Nahrstedt Department of Computer
More informationStorage Layout and I/O Performance in Data Warehouses
Storage Layout and I/O Performance in Data Warehouses Matthias Nicola 1, Haider Rizvi 2 1 IBM Silicon Valley Lab 2 IBM Toronto Lab mnicola@us.ibm.com haider@ca.ibm.com Abstract. Defining data placement
More informationCloud Server. Parallels. An Introduction to Operating System Virtualization and Parallels Cloud Server. White Paper. www.parallels.
Parallels Cloud Server White Paper An Introduction to Operating System Virtualization and Parallels Cloud Server www.parallels.com Table of Contents Introduction... 3 Hardware Virtualization... 3 Operating
More informationStoring Data: Disks and Files. Disks and Files. Why Not Store Everything in Main Memory? Chapter 7
Storing : Disks and Files Chapter 7 Yea, from the table of my memory I ll wipe away all trivial fond records. -- Shakespeare, Hamlet base Management Systems 3ed, R. Ramakrishnan and J. Gehrke 1 Disks and
More informationSelection of the Best DBMS: A Fuzzy based Multi-objective Decision Making Approach
Selection of the Best DBMS: A Fuzzy based Multi-objective Decision Making Approach Abdul Mateen, Basit Raza, Mian Muhammad Awais, Muhammad Sher Abstract Evolution of computer starts from human dependent
More informationHADOOP PERFORMANCE TUNING
PERFORMANCE TUNING Abstract This paper explains tuning of Hadoop configuration parameters which directly affects Map-Reduce job performance under various conditions, to achieve maximum performance. The
More informationAchieving Nanosecond Latency Between Applications with IPC Shared Memory Messaging
Achieving Nanosecond Latency Between Applications with IPC Shared Memory Messaging In some markets and scenarios where competitive advantage is all about speed, speed is measured in micro- and even nano-seconds.
More informationSQL Server Business Intelligence on HP ProLiant DL785 Server
SQL Server Business Intelligence on HP ProLiant DL785 Server By Ajay Goyal www.scalabilityexperts.com Mike Fitzner Hewlett Packard www.hp.com Recommendations presented in this document should be thoroughly
More informationCSE 544 Principles of Database Management Systems. Magdalena Balazinska Fall 2007 Lecture 5 - DBMS Architecture
CSE 544 Principles of Database Management Systems Magdalena Balazinska Fall 2007 Lecture 5 - DBMS Architecture References Anatomy of a database system. J. Hellerstein and M. Stonebraker. In Red Book (4th
More information