Metascheduling of HPC Jobs in Day-Ahead Electricity Markets

High performance grid computing is a key enabler of large scale collaborative computational science. With the promise of exascale computing, high performance grid systems are expected to incur electricity bills that grow super-linearly over time. In order to achieve cost effectiveness in these systems, it is essential for the scheduling algorithms to exploit electricity price variations, both in space and time, that are prevalent in the dynamic electricity price markets. In this paper, we present a metascheduling algorithm to optimize the placement of jobs in a compute grid which consumes electricity from the day-ahead wholesale market. We formulate the scheduling problem as a Minimum Cost Maximum Flow problem and leverage queue waiting time and electricity price predictions to accurately estimate the cost of job execution at a system. Using trace based simulation with real and synthetic workload traces, and real electricity price data sets, we demonstrate our approach on two currently operational grids, XSEDE and NorduGrid. Our experimental setup collectively constitute more than 433K processors spread across 58 compute systems in 17 geographically distributed locations. Experiments show that our approach simultaneously optimizes the total electricity cost and the average response time of the grid, without being unfair to users of the local batch systems.Comment: Appears in IEEE Transactions on Parallel and Distributed System

Optimal Dataflow Scheduling on a Heterogeneous Multiprocessor With Reduced Response Time Bounds

Heterogeneous computing platforms with multiple types of computing resources have been widely used in many industrial systems to process dataflow tasks with pre-defined affinity of tasks to subgroups of resources. For many dataflow workloads with soft real-time requirements, guaranteeing fast and bounded response times is often the objective. This paper presents a new set of analysis techniques showing that a classical real-time scheduler, namely earliest-deadline first (EDF), is able to support dataflow tasks scheduled on such heterogeneous platforms with provably bounded response times while incurring no resource capacity loss, thus proving EDF to be an optimal solution for this scheduling problem. Experiments using synthetic workloads with widely varied parameters also demonstrate that the magnitude of the response time bounds yielded under the proposed analysis is reasonably small under all scenarios. Compared to the state-of-the-art soft real-time analysis techniques, our test yields a 68% reduction on response time bounds on average. This work demonstrates the potential of applying EDF into practical industrial systems containing dataflow-based workloads that desire guaranteed bounded response times

Hybrid Load Balancing Algorithm in Heterogeneous Cloud Environment

Cloud computing is a heterogeneous environment offers a rapidly and on-demand wide range of services to the end users.It's a new solution and strategy for high performance computing where, it achieve high availability, flexibility, cost reduced and on demand scalability. The need to efficient and powerful load balancing algorithms is one of the most important issues in cloud computing to improve the performance. This paper proposed a hybrid load balancing algorithm to improve the performance and efficiency in heterogeneous cloud environment. The algorithm considers the current resource information and the CPU capacity factor and takes advantages of both random and greedy algorithms. The hybrid algorithm has been evaluated and compared with other algorithms using cloud Analyst simulator. The experiment results show that the proposed algorithm improves the average response time and average processing time compared with other algorithms

Thinning and thickening in active microrheology

When pulling a probe particle in a many-particle system with fixed velocity, the probe's effective friction, defined as average pulling force over its velocity, $\gamma_{eff}:=\langle F_{ex}\rangle/u$, first keeps constant (linear response), then decreases (thinning) and finally increases (thickening). We propose a three-time-scales picture (TTSP) to unify thinning and thickening behaviour. The points of the TTSP are that there are three distinct time scales of bath particles: diffusion, damping, and single probe-bath (P-B) collision; the dominating time scales, which are controlled by the pulling velocity, determine the behaviour of the probe's friction. We confirm the TTSP by Langevin dynamics simulation. Microscopically, we find that for computing the effective friction, Maxwellian distribution of bath particles' velocities works in low Reynolds number (Re) but fails in high Re. It can be understood based on the microscopic mechanism of thickening obtained in the $T=0$ limit. Based on the TTSP, we explain different thinning and thickening observations in some earlier literature

SMDP-Based Dynamic Batching for Efficient Inference on GPU-Based Platforms

In up-to-date machine learning (ML) applications on cloud or edge computing platforms, batching is an important technique for providing efficient and economical services at scale. In particular, parallel computing resources on the platforms, such as graphics processing units (GPUs), have higher computational and energy efficiency with larger batch sizes. However, larger batch sizes may also result in longer response time, and thus it requires a judicious design. This paper aims to provide a dynamic batching policy that strikes a balance between efficiency and latency. The GPU-based inference service is modeled as a batch service queue with batch-size dependent processing time. Then, the design of dynamic batching is a continuous-time average-cost problem, and is formulated as a semi-Markov decision process (SMDP) with the objective of minimizing the weighted sum of average response time and average power consumption. The optimal policy is acquired by solving an associated discrete-time Markov decision process (MDP) problem with finite state approximation and "discretization". By introducing an abstract cost to reflect the impact of "tail" states, the space complexity and the time complexity of the procedure can decrease by 63.5% and 98%, respectively. Our results show that the optimal policies potentially possess a control limit structure. Numerical results also show that SMDP-based batching policies can adapt to different traffic intensities and outperform other benchmark policies. Furthermore, the proposed solution has notable flexibility in balancing power consumption and latency.Comment: Accepted by 2023 IEEE International Conference on Communications (ICC

Analisis Kerentanan Dan Kehandalan Layanan Jaringan Cloud Berbasis Platform Eucalyptus

Cloud computing is a computing paradigm that evolves from existing technology, such as grid computing, virtualization and the Internet. Cloud computing provides an illusion of unlimited computing resources, which can be accessed from anywhere, anytime. Despite the potential gains achieved from the cloud computing, the model security is still questionable which hindered adoption. The security problem becomes more complicated under the cloud model as new dimensions have entered into the problem scope related to the model architecture, multi-tenancy, elasticity, and layers dependency stack. Eucalyptus-based cloud network services widely deployed as private cloud infrastructure. Experiment on this paper focused on finding potential denial-of-service (DOS) and the impact on ability to provide services during attack. We observe an increase on response time up to 2863.22% during attack to the web-based management service. Reducing average system load to an acceptable level, help prevents disruption of the service, by implementing rate control and rate limit on cloud controller