Dual-phase just-in-time workflow scheduling in P2P grid systems

This paper presents a fully decentralized justin-time workflow scheduling method in a P2P Grid system. The proposed solution allows each peer node to autonomously dispatch inter-dependent tasks of workflows to run on geographically distributed computers. To reduce the workflow completion time and enhance the overall execution efficiency, not only does each node perform as a scheduler to distribute its tasks to execution nodes (or resource nodes), but the resource nodes will also set the execution priorities for the received tasks. By taking into account the unpredictability of tasks' finish time, we devise an efficient task scheduling heuristic, namely dynamic shortest makespan first (DSMF), which could be applied at both scheduling phases for determining the priority of the workflow tasks. We compare the performance of the proposed algorithm against seven other heuristics by simulation. Our algorithm achieves 20%~60% reduction on the average completion time and 37.5%~90% improvement on the average workflow execution efficiency over other decentralized algorithms. © 2010 IEEE.published_or_final_versionProcessing (ICPP 2010), San Diego, CA., 13-16 September 2010. In Proceedings of the 39th ICCP, 2010, p. 238-24

Minimization of cloud task execution length with workload prediction errors

In cloud systems, it is non-trivial to optimize task’s execution performance under user’s affordable budget, especially with possible workload prediction errors. Based on an optimal algorithm that can minimize cloud task’s execution length with predicted workload and budget, we theoretically derive the upper bound of the task execution length by taking into account the possible workload prediction errors. With such a state-of-the-art bound, the worst-case performance of a task execution with a certain workload prediction errors is predictable. On the other hand, we build a close-to-practice cloud prototype over a real cluster environment deployed with 56 virtual machines, and evaluate our solution with different resource contention degrees. Experiments show that task execution lengths under our solution with estimates of worst-case performance are close to their theoretical ideal values, in both non-competitive situation with adequate resources and the competitive situation with a certain limited available resources. We also observe a fair treatment on the resource allocation among all tasks.published_or_final_versio

Defeating network jitter for virtual machines

Virtualization based cloud computing hosts networked applications in virtual machines (VMs), and provides each VM the desired degree of performance isolation using resource isolation mechanisms. Existing isolation solutions address heavily on resource proportionality such as CPU, memory and I/O bandwidth, but seldom focus on resource provisioning rate. Even the VM is allocated with adequate resources, if they can not be provided in a timely manner, problems such as network jitter will be very serious and significantly affect the performance of cloud applications like internet audio/video streaming. This paper systematically analyzes and illustrates the causes of unpredictable network latency in virtualized execution environments. We decouple the design goals of resource proportionality from resource provisioning rate, and adopt divide-and-conquer strategy to defeat network jitter for VMs: (1) in VMM CPU scheduling, we differentiate self-initiated I/O from event-triggered I/O, and individually map them to periodic and aperiodic real-time domains to schedule them together; (2) in network traffic shaping of VMs, we introduce the concept of smooth window to smooth network latency and apply closed-loop feedback control to maintain network resource consumption. We implement our solutions in Xen 4.1.0 and Linux 2.6.32.13. The experimental results with both real-life applications and low-level benchmarks show that our solutions can significantly reduce network jitter, and meanwhile effectively maintain resource proportionality.published_or_final_versionThe 4th IEEE International Conference on Utility and Cloud Computing (UCC 2011), Victoria, NSW, 5-8 December 2011. In Proceedings of the 4th IEEE-UCC, 2011, p. 65-7

Optimization of Composite Cloud Service Processing with Virtual Machines

By leveraging virtual machine (VM) technology, we optimize cloud system performance based on refined resource allocation, in processing user requests with composite services. Our contribution is three-fold. (1) We devise a VM resource allocation scheme with a minimized processing overhead for task execution. (2) We comprehensively investigate the best-suited task scheduling policy with different design parameters. (3) We also explore the best-suited resource sharing scheme with adjusted divisible resource fractions on running tasks in terms of Proportional-Share Model (PSM), which can be split into absolute mode (called AAPSM) and relative mode (RAPSM). We implement a prototype system over a cluster environment deployed with 56 real VM instances, and summarized valuable experience from our evaluation. As the system runs in short supply, Lightest Workload First (LWF) is mostly recommended because it can minimize the overall response extension ratio (RER) for both sequential-mode tasks and parallel-mode tasks. In a competitive situation with over-commitment of resources, the best one is combining LWF with both AAPSM and RAPSM. It outperforms other solutions in the competitive situation, by 16+% w.r.t. the worst-case response time and by 7.4+% w.r.t. the fairness.published_or_final_versio

On the Performance Prediction of BLAS-based Tensor Contractions

Tensor operations are surging as the computational building blocks for a variety of scientific simulations and the development of high-performance kernels for such operations is known to be a challenging task. While for operations on one- and two-dimensional tensors there exist standardized interfaces and highly-optimized libraries (BLAS), for higher dimensional tensors neither standards nor highly-tuned implementations exist yet. In this paper, we consider contractions between two tensors of arbitrary dimensionality and take on the challenge of generating high-performance implementations by resorting to sequences of BLAS kernels. The approach consists in breaking the contraction down into operations that only involve matrices or vectors. Since in general there are many alternative ways of decomposing a contraction, we are able to methodically derive a large family of algorithms. The main contribution of this paper is a systematic methodology to accurately identify the fastest algorithms in the bunch, without executing them. The goal is instead accomplished with the help of a set of cache-aware micro-benchmarks for the underlying BLAS kernels. The predictions we construct from such benchmarks allow us to reliably single out the best-performing algorithms in a tiny fraction of the time taken by the direct execution of the algorithms.Comment: Submitted to PMBS1

Probabilistic best-fit multi-dimensional range query in Self-Organizing Cloud

With virtual machine (VM) technology being increasingly mature, computing resources in modern Cloud systems can be partitioned in fine granularity and allocated on demand with 'pay-as-you-go' model. In this work, we study the resource query and allocation problems in a Self- Organizing Cloud (SOC), where host machines are connected by a peer-to-peer (P2P) overlay network on the Internet. To run a user task in SOC, the requester needs to perform a multi-dimensional range search over the P2P network for locating host machines that satisfy its minimal demand on each type of resources. The multi-dimensional range search problem is known to be challenging as contentions along multiple dimensions could happen in the presence of the uncoordinated analogous queries. Moreover, low resource matching rate may happen while restricting query delay and network traffic. We design a novel resource discovery protocol, namely Proactive Index Diffusion CAN (PID-CAN), which can proactively diffuse resource indexes over the nodes and randomly route query messages among them. Such a protocol is especially suitable for the range query that needs to maximize its best-fit resource shares under possible competition along multiple resource dimensions. Via simulation, we show that PID-CAN could keep stable and optimized searching performance with low query delay and traffic overhead, for various test cases under different distributions of query ranges and competition degrees. It also performs satisfactorily in dynamic node-churning situation. © 2011 IEEE.published_or_final_versionThe 40th International Conference on Parallel Processing (ICPP-2011), Taipei City, Taiwan, 13-16 September 2011. In Proceedings of the 40th ICPP, 2011, p. 763-77

Towards payment-bound analysis in cloud systems with task-prediction errors

Conference Theme: Change we are leadingIn modern cloud systems, how to optimize user service level based on virtual resources customized on demand is a critical issue. In this paper, we comprehensively analyze the payment bound under a cloud model with virtual machines (VMs), by taking into account that task’s workload may be predicted with errors. The analysis is based on an optimized resource allocation algorithm with polynomial time complexity. We theoretically derive the upper bound of task payment based on a particular margin of workload prediction-error. We also extend the payment-minimization algorithm to adapt to the dynamic changes of host availability over time, and perform the evaluation by a real-cluster environment with 56 VMs deployed. Experiments confirm the correctness of our theoretical inference, and show that our payment-minimization solution can keep 95% of user payments below 1.15 times as large as the theoretical values of the ideal payment with hypothetically accurate information. The ratio for the rest user payments can be limited to about 1.5 at the worst case.postprin

Variation in grouping patterns, mating systems and social structure: what socio-ecological models attempt to explain

Socio-ecological models aim to predict the variation in social systems based on a limited number of ecological parameters. Since the 1960s, the original model has taken two paths: one relating to grouping patterns and mating systems and one relating to grouping patterns and female social structure. Here, we review the basic ideas specifically with regard to non-human primates, present new results and point to open questions. While most primates live in permanent groups and exhibit female defence polygyny, recent studies indicate more flexibility with cooperative male resource defence occurring repeatedly in all radiations. In contrast to other animals, the potential link between ecology and these mating systems remains, however, largely unexplored. The model of the ecology of female social structure has often been deemed successful, but has recently been criticized. We show that the predicted association of agonistic rates and despotism (directional consistency of relationships) was not supported in a comparative test. The overall variation in despotism is probably due to phylogenetic grade shifts. At the same time, it varies within clades more or less in the direction predicted by the model. This suggests that the model's utility may lie in predicting social variation within but not across clades

Callers’ attitudes and experiences of UK breastfeeding helpline support

Background: Breastfeeding peer support, is considered to be a key intervention for increasing breastfeeding duration rates. Whilst a number of national organisations provide telephone based breastfeeding peer support, to date there have been no published evaluations into callers’ experiences and attitudes of this support. In this study we report on the descriptive and qualitative insights provided by 908 callers as part of an evaluation of UK-based breastfeeding helpline(s). Methods: A structured telephone interview, incorporating Likert scale responses and open-ended questions was undertaken with 908 callers over May to August, 2011 to explore callers’ experiences of the help and support received via the breastfeeding helpline(s). Results: Overall satisfaction with the helpline was high, with the vast majority of callers’ recalling positive experiences of the help and support received. Thematic analysis was undertaken on all qualitative and descriptive data recorded during the evaluation, contextualised within the main areas addressed within the interview schedule in terms of ‘contact with the helplines’; ‘experiences of the helpline service’, ‘perceived effectiveness of support provision’ and ‘impact on caller wellbeing’. Conclusion: Callers valued the opportunity for accessible, targeted, non-judgmental and convenient support. Whilst the telephone support did not necessarily influence women’s breastfeeding decisions, the support they received left them feeling reassured, confident and more determined to continue breastfeeding. We recommend extending the helpline service to ensure support can be accessed when needed, and ongoing training and support for volunteers. Further advertising and promotion of the service within wider demographic groups is warranted