A 'cool' load balancer for parallel applications

Sarood, Osman; Kalé, Laxmikant V.

doi:10.1145/2063384.2063412

Cited by 32 publications

(34 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It now supports features such as automatic checkpointing [52,40], communication optimizations [34], fault tolerance, and power-andtemperature optimizations [43].…”

Section: Charm++ and Cse Applicationsmentioning

confidence: 99%

Controlling Concurrency and Expressing Synchronization in Charm++ Programs

Kalé

Lifflander

2014

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Abstract. Charm++ is a parallel programming system that evolved over the past 20 years to become a well-established system for programming parallel science and engineering applications, in addition to the combinatorial search applications with which it started. At its earliest point, the precursor to Charm++, the Chare Kernel, was a purely reactive specification, similar to most actor languages. This paper describes the evolution of a series of concurrency control mechanisms that have been deployed in Charm++ to tame this unrestricted concurrency in order to improve code clarity and/or to improve performance.

show abstract

“…It now supports features such as automatic checkpointing [52,40], communication optimizations [34], fault tolerance, and power-andtemperature optimizations [43].…”

Section: Charm++ and Cse Applicationsmentioning

confidence: 99%

Controlling Concurrency and Expressing Synchronization in Charm++ Programs

Kalé

Lifflander

2014

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recently, Sarood et al [9] proposed models that take cooling energy consumptions into consideration. By reasonably balancing workloads and employing dynamic voltage and frequency scaling (DVFS), they successfully lowered the overall energy consumed by their cooling system while satisfying temperature constraints.…”

Section: A Single Data Centermentioning

confidence: 99%

Profit Aware Load Balancing for Distributed Cloud Data Centers

Liu

Ren

Quan

et al. 2013

2013 IEEE 27th International Symposium on Parallel and Distributed Processing

View full text Add to dashboard Cite

Abstract-The advent of cloud systems has spurred the emergence of an impressive assortment of Internet services. Recent pressures on enhancing the profitability by curtailing surging dollar costs on energy have posed challenges to, as well as placed a new emphasis on, designing energy-efficient request dispatching and resource management algorithms. What further adds to the design challenge is the highly diverse nature of Internet service requests in terms of Quality-of-Service (QoS) constraints and business values. Nonetheless, most of the existing job scheduling and resource management solutions are for a single type of request and are profit oblivious. They are unable to reap the benefit of multi-service profit-aware algorithm designs.In this paper, we consider a cloud service provider operating geographically distributed data centers in a multi-electricitymarket environment, and propose an energy-efficient, profit-and cost-aware request dispatching and resource allocation algorithm to maximize a service provider's net profit. We formulate the net profit maximization issue as a constrained optimization problem, using a unified task model capturing multiple cloud layers (e.g., SaaS, PaaS, IaaS.) The proposed approach maximizes a service provider's net profit by judiciously distributing service requests to data centers, powering on/off an appropriate number of servers, and allocating server resources to dispatched requests. We conduct extensive experiments to validate our proposed algorithm. Results show that our proposed approach can improve a service provider's net profit significantly. I. INTRODUCTIONWith the development of cloud computing, service providers are able to provide a variety of complex applications and services to people's daily lives, such as Google Docs and AppEngine, Amazon EC2 and S3, etc. These applications and services are all supported by service provider's data centers and delivered to a wide range of clients over the Internet.The large number of service requests drastically increases not only the need for data centers, but also the scale of data centers and their energy consumptions. The dollar cost spent on energy consumption takes a large portion of a service provider's operational cost annually. As an example, Google has more than 500K servers and it consumes more than $38M worth of electricity each year. Similarly, Microsoft has more than 200K servers and spends more than $36M on electricity annually [1]. Evidently, dollar costs on energy consumptions have been a critical part in operational cost for service providers. It is fair to say that an efficient computing resource management approach for distributed cloud data centers is essential to service providers.

show abstract

“…Temperature-aware loading balancing strategies are investigated in [20] [21]. Taking into account energy conservation, the strategies limit CPU temperatures to a customized threshold.…”

Section: B Thermal-aware Resource Management Strategiesmentioning

confidence: 99%

“…The CPU factor has been addressed in prior studies (see, for example, [25] [20] [21]). Unfortunately, the thermal impact of disk I/O on data nodes remains an open issue.…”

Section: B Impact Of Cpu and Disks On Inlet/outlet Temperaturesmentioning

confidence: 99%

Thermal modeling and analysis of storage systems

Jiang

Alghamdi

Zhang

et al. 2012

2012 IEEE 31st International Performance Computing and Communications Conference (IPCCC)

View full text Add to dashboard Cite

Abstract-Recognizing that power and cooling cost for data centers are increasing, we address in this study the thermal impact of storage systems. In the first phase of this work, we generate the thermal profile of a storage server containing three hard disks. The profiling results show that disks have comparable thermal impacts as processing and networking elements to overall storage node temperature. We develop a thermal model to estimate the outlet temperature of a storage server based on processor and disk utilizations. The thermal model is validated against data acquired by an infrared thermometer as well as build-in temperature sensors on disks. Next, we apply the thermal model to investigate the thermal impact of workload management on storage systems. Our study suggests that disk-aware thermal management techniques have significant impacts on reducing cooling cost of storage systems. We further show that this work can be extended to analysis the cooling cost of data centers with massive storage capacity.

show abstract

A 'cool' load balancer for parallel applications

Cited by 32 publications

References 16 publications

Controlling Concurrency and Expressing Synchronization in Charm++ Programs

Controlling Concurrency and Expressing Synchronization in Charm++ Programs

Profit Aware Load Balancing for Distributed Cloud Data Centers

Thermal modeling and analysis of storage systems

Contact Info

Product

Resources

About