Showing papers by "Lars Lundberg published in 2016"

Energy-Aware Adaptation in Managed Cassandra Datacenters

Abstract: Today, Apache Cassandra, an highly scalable and available NoSql datastore, is largely used by enterprises of each size and for application areas that range from entertainment to big data analytics. Managed Cassandra service providers are emerging to hide the complexity of the installation, fine tuning and operation of Cassandra datacenters. As for all complex services, human assisted management of a multi-tenant cassandra datacenter is unrealistic. Rather, there is a growing demand for autonomic management solutions. In this paper, we present an optimal energy-aware adaptation model for managed Cassandra datacenters that modify the system configuration orchestrating three different actions: horizontal scaling, vertical scaling and energy aware placement. The model is built from a real case based on real application data from Ericsson AB. We compare the performance of the optimal adaptation with two heuristics that avoid system perturbations due to re-configuration actions triggered by subscription of new tenants and/or changes in the SLA. One of the heuristic is local optimisation and the second is a best fit decreasing algorithm selected as reference point because representative of a wide range of research and practical solutions. The main finding is that heuristic's performance depends on the scenario and workload and no one dominates in all the cases. Besides, in high load scenarios, the suboptimal system configuration obtained with an heuristic adaptation policy introduce a penalty in electric energy consumption in the range [+25%, +50%] if compared with the energy consumed by an optimal system configuration.

An Energy-Aware Adaptation Model for Big Data Platforms

Abstract: Platforms for big data includes mechanisms and tools to model, organize, store and access big data (e.g. Apache Cassandra, Hbase, Amazon SimpleDB, Dynamo, Google BigTable). The resource management for those platforms is a complex task and must account also for multi-tenancy and infrastructure scalability. Human assisted control of Big data platform is unrealistic and there is a growing demand for autonomic solutions. In this paper we propose a QoS and energy-aware adaptation model designed to cope with the real case of a Cassandra-as-a-Service provider.

Performance Evaluation of Trajectory Queries on Multiprocessor and Cluster

Abstract: In this study, we evaluate the performance of trajectory queries that are handled by Cassandra, MongoDB, and PostgreSQL. The evaluation is conducted on a multiprocessor and a cluster. T ...

Comparing Automatic Load Balancing Using VMware DRS with a Human Expert

Abstract: In recent years, there has been a rapid growth of interest in dynamic management of resources in virtualized systems. Virtualization provides great flexibility in terms of resource sharing but at the same time it also brings new challenges for load balancing using automatic migrations of virtual machines. In this paper, we have evaluated VMware's Distributed Resource Scheduler (DRS) in a number of realistic scenarios using multiple instances of a large industrial telecommunication application. We have measured the performance on the hosts before and after the migration in terms of CPU utilization, and compared DRS migrations with human expert migrations. According to our results, DRS with the most aggressive threshold gave us the best results. It could balance the load in 40% of cases while in other cases it could not balance the load properly. DRS did completely unnecessary migrations back and forth in some cases.

Performance Implications of Resource Over-Allocation During the Live Migration

Abstract: As the number of cloud users are increasing, it becomes essential for cloud service providers to allocate the right amount of resources to virtual machines, especially during live migration. In order to increase the resource utilization and reduce waste, the providers have started to think about the role of over-allocating the resources. However, the benefits of over-allocations are not without inherent risks. In this paper, we conducted an experiment using a large telecommunication application that runs inside virtual machines, here we have varied the number of vCPU resources allocated to these virtual machines in order to find the best choice which at the same time reduces the risk of underallocating resources after the migration and increases the performance during the live migration. During our measurements we have used VMware's vMotion to migrate virtual machines while they are running. The results of this study will help virtualized environment service providers to decide how much resources should be allocated for better performance during live migration as well as how much resource would be required for a given load.

Performance evaluation of trajector y q ueries on multiprocessor and cluste r

Abstract: In this study, we evaluate the performance of trajectory queries that are handled by Cassandra, MongoDB, and PostgreSQL. The evaluation is conducted on a multiprocessor and a cluster. Telecommunication companies collect a lot of data from their mobile users. These data must be analysed in order to support business decisions, such as infrastructure planning. The optimal choice of hardware platform and database can be different from a query to another. We use data collected from Telenor Sverige, a telecommunication company that operates in Sweden. These data are collected every five minutes for an entire week in a medium sized city. The execution time results show that Cassandra performs much better than MongoDB and PostgreSQL for queries that do not have spatial features. Statio’s Cassandra Lucene index incorporates a geospatial index into Cassandra, thus making Cassandra to perform similarly as MongoDB to handle spatial queries. In four use cases, namely, distance query, k-nearest neigbhor query, range query, and region query, Cassandra performs much better than MongoDB and PostgreSQL for two cases, namely range query and region query. The scalability is also good for these two use cases.