An Energy-Aware Task Scheduling Algorithm for a Heterogeneous Data Center

Zhang, Shuo; Wang, Baosheng; Zhao, Baokang; Jing, Tao

doi:10.1109/trustcom.2013.178

Cited by 8 publications

(5 citation statements)

References 16 publications

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“…Such requests are inherently multi-dimensional and simultaneously ask for multiple resources such as processor cycles, network bandwidth, and storage space [23], [27], [34] (see also multi-dimensional load balancing in virtualization [28], [32]). In addition to the multi-dimensionality of resource requests, another challenge is the heterogeneity of server clusters because of incremental hardware deployment and the use of dedicated specialized hardware for particular tasks [1], [24], [45]. As a third source of non-uniformity, the objective of the load balancing exercise is often defined by the application at hand and the resource being allocated.…”

Section: Introductionmentioning

confidence: 99%

Tight Bounds for Online Vector Scheduling

Kell

Kulkarni

et al. 2015

2015 IEEE 56th Annual Symposium on Foundations of Computer Science

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Modern data centers face a key challenge of effectively serving user requests that arrive online. Such requests are inherently multi-dimensional and characterized by demand vectors over multiple resources such as processor cycles, storage space, and network bandwidth. Typically, different resources require different objectives to be optimized, and Lr norms of loads are among the most popular objectives considered. Furthermore, the server clusters are also often heterogeneous making the scheduling problem more challenging.To address these problems, we consider the online vector scheduling problem in this paper. Introduced by Chekuri and Khanna (SIAM J. of Comp. 2006), vector scheduling is a generalization of classical load balancing, where every job has a vector load instead of a scalar load. The scalar problem, introduced by Graham in 1966, and its many variants (identical and unrelated machines, makespan and Lr-norm optimization, offline and online jobs, etc.) have been extensively studied over the last 50 years. In this paper, we resolve the online complexity of the vector scheduling problem and its important generalizations -for all Lr norms and in both the identical and unrelated machines settings. Our main results are:• For identical machines, we show that the optimal competitive ratio is Θ(log d/ log log d) by giving an online lower bound and an algorithm with an asymptotically matching competitive ratio. The lower bound is technically challenging, and is obtained via an online lower bound for the minimum mono-chromatic clique problem using a novel online coloring game and randomized coding scheme. Our techniques also extend to asymptotically tight upper and lower bounds for general Lr norms.• For unrelated machines, we show that the optimal competitive ratio is Θ(log m + log d) by giving an online lower bound that matches a previously known upper bound. Unlike identical machines, however, extending these results, particularly the upper bound, to general Lr norms requires new ideas. In particular, we use a carefully constructed potential function that balances the individual Lr objectives with the overall (convexified) min-max objective to guide the online algorithm and track the changes in potential to bound the competitive ratio.

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Section: Introductionmentioning

confidence: 99%

Tight Bounds for Online Vector Scheduling

Kell

Kulkarni

et al. 2015

2015 IEEE 56th Annual Symposium on Foundations of Computer Science

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“…In recent years, there are a series of research works to study the task scheduling problem in CPCSs, an energyaware task scheduling algorithm based on the greedy algorithm is proposed for the heterogeneous cloud in [16]. To realize the implementation of scientific workflows, a novel energy-efficient resource allocation scheme was proposed in response to the expanding cloud [17].…”

Section: Related Workmentioning

confidence: 99%

PDM: Privacy-Aware Deployment of Machine-Learning Applications for Industrial Cyber–Physical Cloud Systems

Yin

et al. 2021

IEEE Trans. Ind. Inf.

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The cyber-physical cloud systems (CPCSs) release powerful capability in provisioning the complicated industrial services. Due to the advances of machine learning in attack detection, a wide range of machine-learning applications are involved in industrial CPCSs. However, how to ensure the implementation efficiency of these applications, and meanwhile avoid the privacy disclosure of the datasets due to data acquisition by different operators, remain challenging for the design of the CPCSs. To fill this gap, a privacy-aware deployment method, named PDM, is devised for hosting the machine-learning applications in the industrial CPCSs. In PDM, the machine-learning applications are partitioned as multiple computing tasks with certain execution order, like workflows. Specifically, the deployment problem is formulated as a multi-objective problem for improving the implementation performance and resource utility. Then the most balanced and optimal strategy is selected by leveraging an improved differential evolution technique. Finally, through comprehensive experiments and comparison analysis, PDM is fully evaluated.

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“…In general, the most intensive phase in EnReal was the resource monitoring that takes quadratic time complexity. Furthermore, the performance evaluation results showed that EnReal outperformed the modi ed energy-aware Greedy-D (Zhang et al 2013) algorithm in terms of energy e ciency by 18% in average.…”

Section: Energy Aware Scheduling For Multiple Workflowsmentioning

confidence: 99%

Multiple Workflows Scheduling in Multi-tenant Distributed Systems

2020

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e work ow is a general notion representing the automated processes along with the ow of data. e automation ensures the processes being executed in the order. erefore, this feature a racts users from various background to build the work ow. However, the computational requirements are enormous and investing for a dedicated infrastructure for these work ows is not always feasible. To cater to the broader needs, multi-tenant platforms for executing work ows were began to be built. In this paper, we identify the problems and challenges in the multiple work ows scheduling that adhere to the platforms. We present a detailed taxonomy from the existing solutions on scheduling and resource provisioning aspects followed by the survey of relevant works in this area. We open up the problems and challenges to shove up the research on multiple work ows scheduling in multi-tenant distributed systems.

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An Energy-Aware Task Scheduling Algorithm for a Heterogeneous Data Center

Cited by 8 publications

References 16 publications

Tight Bounds for Online Vector Scheduling

Tight Bounds for Online Vector Scheduling

PDM: Privacy-Aware Deployment of Machine-Learning Applications for Industrial Cyber–Physical Cloud Systems

Multiple Workflows Scheduling in Multi-tenant Distributed Systems

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