CloudScope: diagnosing and managing performance interference in multi-tenant clouds

© 2015 IEEE.Virtual machine consolidation is attractive in cloud computing platforms for several reasons including reduced infrastructure costs, lower energy consumption and ease of management. However, the interference between co-resident workloads caused by virtualization can violate the service level objectives (SLOs) that the cloud platform guarantees. Existing solutions to minimize interference between virtual machines (VMs) are mostly based on comprehensive micro-benchmarks or online training which makes them computationally intensive. In this paper, we present CloudScope, a system for diagnosing interference for multi-tenant cloud systems in a lightweight way. CloudScope employs a discrete-time Markov Chain model for the online prediction of performance interference of co-resident VMs. It uses the results to optimally (re)assign VMs to physical machines and to optimize the hypervisor configuration, e.g. the CPU share it can use, for different workloads. We have implemented CloudScope on top of the Xen hypervisor and conducted experiments using a set of CPU, disk, and network intensive workloads and a real system (MapReduce). Our results show that CloudScope interference prediction achieves an average error of 9%. The interference-aware scheduler improves VM performance by up to 10% compared to the default scheduler. In addition, the hypervisor reconfiguration can improve network throughput by up to 30%

Balancing antagonistic time and resource utilization constraints in over-subscribed scheduling problems

In this paper, we report work aimed at applying concepts of constraint-based problem structuring and multi-perspective scheduling to an over-subscribed scheduling problem. Previous research has demonstrated the utility of these concepts as a means for effectively balancing conflicting objectives in constraint-relaxable scheduling problems, and our goal here is to provide evidence of their similar potential in the context of HST observation scheduling. To this end, we define and experimentally assess the performance of two time-bounded heuristic scheduling strategies in balancing the tradeoff between resource setup time minimization and satisfaction of absolute time constraints. The first strategy considered is motivated by dispatch-based manufacturing scheduling research, and employs a problem decomposition that concentrates local search on minimizing resource idle time due to setup activities. The second is motivated by research in opportunistic scheduling and advocates a problem decomposition that focuses attention on the goal activities that have the tightest temporal constraints. Analysis of experimental results gives evidence of differential superiority on the part of each strategy in different problem solving circumstances. A composite strategy based on recognition of characteristics of the current problem solving state is then defined and tested to illustrate the potential benefits of constraint-based problem structuring and multi-perspective scheduling in over-subscribe scheduling problems

Decentralised Learning MACs for Collision-free Access in WLANs

By combining the features of CSMA and TDMA, fully decentralised WLAN MAC schemes have recently been proposed that converge to collision-free schedules. In this paper we describe a MAC with optimal long-run throughput that is almost decentralised. We then design two \changed{schemes} that are practically realisable, decentralised approximations of this optimal scheme and operate with different amounts of sensing information. We achieve this by (1) introducing learning algorithms that can substantially speed up convergence to collision free operation; (2) developing a decentralised schedule length adaptation scheme that provides long-run fair (uniform) access to the medium while maintaining collision-free access for arbitrary numbers of stations

Dynamic Machine Level Resource Allocation to Improve Tasking Performance Across Multiple Processes

Across the landscape of computing, parallelism within applications is increasingly important in order to track advances in hardware capability and meet critical performance metrics. However, writing parallel applications is difficult to do in a scalable way, which has led to the creation of tasking libraries and language extensions like OpenMP, Intel Threading Building Blocks, Qthreads, and more. These tools abstract parallel execution by expressing it in terms of work units (tasks) rather than specific hardware details. This abstraction enables scaling and allows programmers to write software solutions that can leverage whatever level of parallelism is available.However, the typical task scheduler is greedy and naïve. Thus, concurrent parallel processes compete for computational resources, which results in unnecessary context switches, mis-timed synchronization, unnecessary resource contention, and the associated consequences. By providing a mechanism of communication between the task schedulers, processes can cooperate to more effectively utilize hardware and avoid the negative consequences of coarse-grained resource contention. This work uses Qthreads to demonstrate that cooperative allocation of computational resources reduces contention and decreases execution time. The overhead added for the resource allocation is shown to have minimal impact. Using the Unbalanced Tree Search (UTS) and High Performance Conjugate Gradient (HPCG) benchmarks, execution time across concurrent processes shows significant decreases across a range of machines running a variety of hardware resources and software configurations. Tests also indicate that dynamic compute-resource allocation provides a clear performance benefit even when hardware resources are oversubscribed: when there are more processes than processing units. UTS tests saw an average of 4.98% reduction in execution time in Linux compared to Qthread\u27s yielding option and an 89.32% reduction in execution time in Apple OS X. HPCG resulted in partitioning reducing execution time by an average of 22.31% compared to the default Qthreads configuration across all test platforms

Hybrid approach for energy aware management of multi-cloud architecture integrating user machines

International audienceThe arrival and development of remotely accessible services via the cloud has transfigured computer technology. However, its impact on personal computing remains limited to cloud-based applications. Meanwhile, acceptance and usage of telephony and smartphones have exploded. Their sparse administration needs and general user friendliness allows all people, regardless of technology literacy, to access, install and use a large variety of applications.We propose in this paper a model and a platform to offer personal computing a simple and transparent usage similar to modern telephony. In this model, user machines are integrated within the classical cloud model, consequently expanding available resources and management targets. In particular, we defined and implemented a modular architecture including resource managers at different levels that take into account energy and QoS concerns. We also propose simulation tools to design and size the underlying infrastructure to cope with the explosion of usage. Functionalities of the resulting platform are validated and demonstrated through various utilization scenarios. The internal scheduler managing resource usage is experimentally evaluated and compared with classical method-ologies, showing a significant reduction of energy consumption with almost no QoS degradation