Modern High-Performance Computing (HPC) centers are facing significant challenges in ingesting large and diverse data streams, leading to bandwidth bottlenecks and scientific delays. To address the shortcomings of traditional static allocation and simple queuing methods, this work introduces a dynamic, value-based approach to bandwidth allocation. We propose two new auction-based mechanisms: the computationally efficient Greedy Value Density Auction and the theoretically robust Vickrey-Clarke-Groves (VCG) Knapsack Auction. Both mechanisms utilize user-provided bids that specify data requirements and scientific value, with the goal of maximizing the total value of successful data transfers. Our simulation results, based on realistic workload characteristics, demonstrate that these auction mechanisms significantly outperform standard First-Come, First-Served (FCFS) baselines. In high-load scenarios, our methods reduce average and tail completion delays by over 80% and improve predictability by decreasing the coefficient of variation of delay by up to 85%. Furthermore, network stability is enhanced, with the peak-to-average load ratio dropping by as much as 70%. This value-driven, adaptive strategy alleviates congestion, improves bandwidth utilization, and ensures that access is prioritized based on scientific importance, ultimately accelerating scientific discovery.

The growing necessity for enhanced processing capabilities in edge devices with limited resources has led us to develop effective methods for improving high-performance computing (HPC) applications. In this paper, we introduce LASP (Lightweight Autotuning of Scientific Application Parameters), a novel strategy designed to address the parameter search space challenge in edge devices. Our strategy employs a multi-armed bandit (MAB) technique focused on online exploration and exploitation. Notably, LASP takes a dynamic approach, adapting seamlessly to changing environments. We tested LASP with four HPC applications: Lulesh, Kripke, Clomp, and Hypre. Its lightweight nature makes it particularly well-suited for resource-constrained edge devices. By employing the MAB framework to efficiently navigate the search space, we achieved significant performance improvements while adhering to the stringent computational limits of edge devices. Our experimental results demonstrate the effectiveness of LASP in optimizing parameter search on edge devices.

Significant power consumption is one of the major challenges for current and future high-performance computing (HPC) systems. All the while, HPC systems generally remain power underutilized, making them a great candidate for applying power oversubscription to reclaim unused capacity. However, an oversubscribed HPC system may occasionally get overloaded. In this paper, we propose MPR (Market-based Power Reduction), a scalable market-based approach where users actively participate in reducing the HPC system’s power consumption to mitigate overloads. In MPR, HPC users bid to supply, in exchange for incentives, the resource reduction required for handling the overloads. Using several real-world trace-based simulations, we extensively evaluate MPR and show that, by participating in MPR, users always receive more rewards than the cost of performance loss. At the same time, the HPC manager enjoys orders of magnitude more resource gain than her incentive payoff to the users. We also demonstrate the real-world effectiveness of MPR on a prototype system.

Cloud applications are increasingly moving away from monolithic services to agile microservices-based deployments. However, efficient resource management for microservices poses a significant hurdle due to the sheer number of loosely coupled and interacting components. The interdependencies between various microservices make existing cloud resource autoscaling techniques ineffective. Meanwhile, machine learning (ML) based approaches that try to capture the complex relationships in microservices require extensive training data and cause intentional SLO violations. Moreover, these ML-heavy approaches are slow in adapting to dynamically changing microservice operating environments. In this paper, we propose PEMA (Practical Efficient Microservice Autoscaling), a lightweight microservice resource manager that finds efficient resource allocation through opportunistic resource reduction. PEMA's lightweight design enables novel workload-aware and adaptive resource management. Using three prototype microservice implementations, we show that PEMA can find efficient resource allocation and save up to 33% resources compared to the commercial rule-based resource allocations.

High performance computing (HPC) systems are large-scale computing systems with thousands of compute nodes. Massive energy consumption is a critical issue for HPC systems. In this paper, we develop an auction mechanism model for energy consumption reduction in an HPC system. Our proposed model includes an optimized resource allocation scheme for HPC jobs based on processor frequency and a Vickery-Clarke-Groove (VCG)-based forward auction model to enable energy reduction participation from HPC users. The model ensures truthful participation from HPC users, where users benefit from revealing their true valuation of energy reduction. We implement a job scheduler simulator and our mechanism model on a parallel discrete-event simulation engine. Through trace-based simulation, we demonstrate the effectiveness of our auction mechanism model. Simulation shows that our model can achieve overall energy reduction for an HPC system, while ensuring truthful participation from the users.

As the critical infrastructure for supporting Internet and cloud computing services, massive geo-distributed data centers are notorious for their huge electricity appetites and carbon footprints. Nonetheless, a lesser-known fact is that data centers are also “thirsty”: to operate data centers, millions of gallons of water are required for cooling and electricity production. The existing water-saving techniques primarily focus on improved “engineering” (e.g., upgrading to air economizer cooling, diverting recycled/sea water instead of potable water) and do not apply to all data centers due to high upfront capital costs and/or location restrictions. In this paper, we propose a software-based approach towards water conservation by exploiting the inherent spatio-temporal diversity of water efficiency across geo-distributed data centers. Specifically, we propose a batch job scheduling algorithm, called WACE (minimization of WAter, Carbon and Electricity cost), which dynamically adjusts geographic load balancing and resource provisioning to minimize the water consumption along with carbon emission and electricity cost while satisfying average delay performance requirement. WACE can be implemented online without foreseeing the far future information and yields a total cost (incorporating electricity cost, water consumption and carbon emission) that is provably close to the optimal algorithm with lookahead information. Finally, we validate WACE through a trace-based simulation study and show that WACE outperforms state-of-the-art benchmarks: 25 percent water saving while incurring an acceptable delay increase. We also extend WACE to joint scheduling of batch workloads and delay-sensitive interactive workloads for further water footprint reduction in geo-distributed data centers.

Demand response refers to reducing energy consumption of participating systems in response to transient surge in power demand or other emergency events. Demand response is particularly important for maintaining power grid transmission stability, as well as achieving overall energy saving. High Performance Computing (HPC) systems can be considered as ideal participants for demand-response programs, due to their massive energy demand. However, the potential loss of performance must be weighed against the possible gain in power system stability and energy reduction. In this paper, we explore the opportunity of demand response on HPC systems by proposing a new HPC job scheduling and resource provisioning model. More specifically, the proposed model applies power-bound energy-conservation job scheduling during the critical demand-response events, while maintaining the traditional performance-optimized job scheduling during the normal period. We expect such a model can attract willing participation of the HPC systems in the demand response programs, as it can improve both power stability and energy saving without significantly compromising application performance. We implement the proposed method in a simulator and compare it with the traditional scheduling approach. Using trace-driven simulation, we demonstrate that the HPC demand response is a viable approach toward power stability and energy savings with only marginal increase in the jobs' execution time.

Interconnection network is a critical component of high-performance computing architecture and application co-design. For many scientific applications, the increasing communication complexity poses a serious concern as it may hinder the scaling properties of these applications on novel architectures. It is apparent that a scalable, efficient, and accurate interconnect model would be essential for performance evaluation studies. In this paper, we present an interconnect model for predicting the performance of large-scale applications on high-performance architectures. In particular, we present a sufficiently detailed interconnect model for Cray's Gemini 3-D torus network. The model has been integrated with an implementation of the Message-Passing Interface (MPI) that can mimic most of its functions with packet-level accuracy on the target platform. Extensive experiments show that our integrated model provides good accuracy for predicting the network behavior, while at the same time allowing for good parallel scaling performance.

Demand response programs maintain transmission stability in power grid through reducing electricity use during peak period, making grid more efficient and robust. While numerous demand response programs are currently being deployed by utility companies, we focus on emergency demand response program, which is critical to ensure reliability during emergency situations. As a key participant in such program, we consider a critical type of data center: multi-tenant colocation data center (or colocation), where multiple tenants mange their own servers in shared space but typically lack incentives to reduce energy for demand response. To enable multi-tenant data center demand response, we propose a contract-based mechanism, called Contract-DR, which offers financial incentives to tenants to shed energy during emergency situations, reducing the usage of cost-ineffective and environmentally-unfriendly diesel generation. We conduct theoretical analysis to prove the optimality of Contract-DR and also validate it through a trace-based study.

Mobile applications can be enhanced to a great extent by using the offloading mechanism in an energy efficient manner to the bounty resourceful clouds. Due to the huge demand of smart phones, the issue of providing more processing capability to this resource constraint device is getting more concern now-a-days. In this paper, a method level offloading mechanism has been proposed where no prior image of the mobile device is needed to be transferred to the cloud. The application is partitioned at different points where the migration of the execution thread is performed from mobile device to nearby resourceful cloud to get the best execution performance in optimal energy cost. The mobile can complete the execution after the partitioned thread returns back from the cloud to the device. This mechanism increases scalability as well as performance in the form of faster execution speed of the mobile devices. Moreover, we consider the mobility of the mobile device and propose a solution to find the best cloud instance on the move. To find out which cloud to offload, the communication latency, capacity, and current load at individual clouds are considered to find out the best cloud to offload to ensure better service for the mobile device. The proposed solution has been simulated and compared against CloneCloud in two different simulation scenarios where we show that our method performs superior to CloneCloud.