By Jan Schaffner
With the proliferation of Software-as-a-Service (SaaS) choices, it truly is changing into more and more very important for person SaaS companies to function their prone at a low in cost. This booklet investigates SaaS from the viewpoint of the supplier and indicates how operational bills may be diminished through the use of “multi tenancy,” a strategy for consolidating numerous consumers onto a small variety of servers.
Specifically, the publication addresses multi tenancy at the database point, concentrating on in-memory column databases, that are the spine of many very important new firm purposes. For successfully imposing multi tenancy in a farm of databases, primary demanding situations needs to be addressed, (i) workload modeling and (ii) info placement. the 1st contains estimating the (shared) source intake for multi tenancy on a unmarried in-memory database server. the second one is composed in assigning tenants to servers in a manner that minimizes the variety of required servers (and therefore bills) in line with the assumed workload version. This step additionally includes replicating tenants for functionality and excessive availability. This booklet provides novel strategies to either problems.
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Extra info for Multi Tenancy for Cloud-Based In-Memory Column Databases: Workload Management and Data Placement
Again, the tenants in the sample are heterogeneous. The sample is split into training data and verification data. e. for fitting the coefficients of Eq. 6)), while the verification data is solely used for quantifying the accuracy of the model. Both training and verification data are shown in Fig. 5. As can be seen in the figure, the prediction has a high accuracy for 99-th percentile values less than 1,000 ms, which is the range of particular interest to us. Larger 99-th percentile values result in a violation of our performance SLO regardless by how much the 99-th percentile value exceeds the 1,000 ms mark.
Among the remaining queries, the one with the highest response time is called the 99-th percentile value. 99-th percentile response times have also been used as a performance metric by other distributed data management systems such as Amazon Dynamo . Note that enforcing a certain 99-th percentile response time is a much stronger performance guarantee than asserting a specific average response time: when looking at response times as a statistical distribution, the values around the 99-th percentile can be considered outliers given the typically much smaller mean value of the distribution.
7 shows the response times in the 99-th percentile that were measured for different sets of heterogeneous tenants and varying values for L, similar to the experiment in the previous section, but this time including periodic writes. 1 Aggregate Scan Capacity Consumption Fig. 5 Coefficients for Eq. 5 7,621 contain writes) are also shown in the figure. As a consequence of the batch writes, the capacity of the server is saturated at a lower read request rate level. The slope of the curve begins to grow exponentially already at a lower value for L in the presence of writes.