25 research outputs found
On the Role of Performance Interference in Consolidated Environments
No full textWith the advent of resource shared environments such as the Cloud, virtualization has become the de facto standard for server consolidation. While consolidation improves utilization, it causes performance-interference between Virtual Machines (VMs) from contention in shared resources such as CPU, Last Level Cache (LLC) and memory bandwidth. Over-provisioning resources for performance sensitive applications can guarantee Quality of Service (QoS), however, it results in low machine utilization. Thus, assuring QoS for performance sensitive applications while allowing co-location has been a challenging problem. In this thesis, we identify ways to mitigate performance interference without undue over-provisioning and also point out the need to model and account for performance interference to improve the reliability and accuracy of elastic scaling. The end goal of this research is to leverage on the observations to provide efficient resource management that is both performance and cost aware. Our main contributions are threefold; first, we improve the overall machine utilization by executing best-effort applications along side latency critical applications without violating its performance requirements. Our solution is able to dynamically adapt and leverage on the changing workload/phase behaviour to execute best-effort applications without causing excessive interference on performance; second, we identify that certain performance metrics used for elastic scaling decisions may become unreliable if performance interference is unaccounted. By modelling performance interference, we show that these performance metrics become reliable in a multi-tenant environment; and third, we identify and demonstrate the impact of interference on the accuracy of elastic scaling and propose a solution to significantly minimise performance violations at a reduced cost.QC 20160927</p
Acceleration of Functional Validation using GPGPU
No full textLogic simulation of a VLSI chip is a computationally intensive process. There exists an urgent need to map functional validation algorithms onto parallel architectures to aid hardware designers in meeting time-to-market constraints. In this paper, we propose three novel methods for logic simulation of combinational circuits on GPGPUs. Initial experiments run on two methods using benchmark circuits using NVIDIA GPGPUs suggest that these methods can be used for accelerating the EDA design flow process
A Monitoring system for community-lab
No full textCommunity-Lab is an open and distributed infrastructure that provides a testbed for researchers to carry out experiments within wireless community networks. Community networks are an emergent model of infrastructures built with off-the-shelf communication equipment that aims to satisfy a community's demand for Internet access and ICT services. Community-Lab consists of a set of nodes integrated into the existing community networks to give researchers access to the network and to allow them to perform experiments. The challenging environment of community networks needs a careful evaluation of experimental data to understand application behavior and spot any misbehavior or anomalies. This paper focuses on demonstrating a monitoring system tailored to meet the specific requirements of the testbed and proposes an architecture for self management to automate management. This demonstration aims to present the current status of the monitoring system, the data gathered and also invite others to experiment with the data generated by the monitoring system
Elastic Scaling in the Cloud: A Multi-tenant Perspective
No full textPerformance interference in the hosting platform introduces uncertainty in the performance guarantees of provisioned services. Existing elasticity controllers are either unaware of this interference or over-provision resources to meet the SLO. In this paper, we take a holistic view on elastic scaling from a multi-tenant perspective. We show that performance interference can significantly impact the accuracy of scaling and result in long periods of SLO violation. Using Memcached as a case-study, we show that making an elasticity controller interference aware can improve the accuracy of scaling decisions and significantly reduce the periods of SLO violation.</p
Resource-aware scaling of multi-threaded Java applications in multi-tenancy scenarios
No full textCloud platforms are becoming more prevalent in every computational domain, particularly in e-Science. A typical scientific workload will have a long execution time or be data intensive. Providing an execution environment for these applications, which belong to different tenants, has to deal with the horizontal scaling of execution flows (i.e. threads) and an effective allocation of resources that takes into account the effective progress made by each tenant. While this is trivial for Bag-of-Tasks and embarrassingly parallel jobs, it is hard for HPC single-process multi-threaded applications because they cannot be scaled up automatically just by adding more virtual machines to execute the workload. In this paper we present MengTian, a distributed execution environment or platform capable of addressing the issues above. It encompasses several extensions to the Java execution environment, ranging from middleware to the virtual machine code and libraries. Our Java-based platform provides a Single System Image abstraction supported by a Partially Global Address Space to transparently spawn threads across a cluster of machines. It monitors progress with different levels-of-detail and accounts and restricts resource consumption. The overall goal is to redistribute resources among different JVM instances, increasing the unitary outcome of the progress vs. resource usage ratio over time.Peer ReviewedPostprint (published version
Energy efficiency dilemma: P2P-cloud vs. datacenter
No full textEnergy consumption is increasing in the IT sector and a remarkable part of this energy is consumed in data centers. Numerous techniques have been proposed to solve the energy efficiency issue in cloud systems. Recently, there are some efforts to decentralize the cloud via distributing data centers in diverse geographical positions. In this paper, we elaborate on the energy consumption of different cloud architectures, from a mega-datacenter to a P2P-cloud that provides extreme decentralization in terms of datacenter size. P2P-cloud is defined as a set of commodity host machines, connected to each other to serve a community. Our evaluation results reveal the fact that the more decentralized the system is, the less energy may be consumed in the system. Studying the energy efficiency of P2P-cloud infrastructure shows that the additional system design complexity involved is warranted with improved energy-efficiency and better locality for some services. Our analysis indicates that such P2P-cloud outperforms the classic datacenter model as long as it meets the locality conditions, which are commonplace in communities. Moreover, we illustrate how much energy can be saved for MapReduce applications with a diverse range of specifications by switching to P2P-cloud.Peer ReviewedPostprint (published version
Augmenting elasticity controllers for improved accuracy
No full textElastic resource provisioning is used to guarantee service level objectives (SLO) at reduced cost in a Cloud platform. However, performance interference in the hosting platform introduces uncertainty in the performance guarantees of provisioned services. Existing elasticity controllers are either unaware of this interference or overprovision resources to meet the SLO. In this paper, we show that assuming predictable performance of VMs in a multi-tenant environment to scale, will result in long periods of SLO violations. We augment the elasticity controller to be aware of interference and improve the convergence time of scaling without over provisioning. We perform experiments with Memcached and compare our solution against a baseline elasticity controller that is unaware of
performance interference. Our results show that augmentation can reduce SLO violations by 65% or more and also save provisioning costs compared to an interference oblivious controller.Peer ReviewedPostprint (published version
Hubbub-scale: towards reliable elastic scaling under multi-tenancy
No full textElastic resource provisioning is used to guarantee service level objective (SLO) with reduced cost in a Cloud platform. However, performance interference in the hosting platform introduces uncertainty in the performance guarantees of provisioned services. Existing elasticity controllers are either unaware of this interference or overprovision resources to meet the SLO. In this paper, we show that assuming predictable performance of VMs to build an elasticity controller will fail if interference is not modelled. We identify and control the different sources of unpredictability and build Hubbub-Scale, an elasticity controller that is reliable in the presence of performance interference. Our evaluation with Redis and Memcached show that Hubbub-Scale efficiently conforms to the SLO requirements under scenarios where standard modelling approaches fail.Peer ReviewedPostprint (published version
