Cost-Efficient Scheduling Heuristics for Deadline Constrained Workloads on Hybrid Clouds

Bossche, Ruben Van den; Vanmechelen, Kurt; Broeckhove, J.

doi:10.1109/cloudcom.2011.50

Cited by 74 publications

(33 citation statements)

References 11 publications

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“…Another work close to our resource allocation policy is [10] that employs a cost-efficient scheduling heuristics to meet the deadline. However, this work is sensitive to execution time estimation error, whereas our work self-tunes based on feedback.…”

Section: Related Work On Cloud Resource Managementmentioning

confidence: 99%

A simulation as a service cloud middleware

et al. 2015

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Many seemingly simple questions that individual users face in their daily lives may actually require substantial number of computing resources to identify the right answers. For example, a user may want to determine the right thermostat settings for different rooms of a house based on a tolerance range such that the energy consumption and costs can be maximally reduced while still offering comfortable temperatures in the house. Such answers can be determined through simulations. However, some simulation models as in this example are stochastic, which require the execution of a large number of simulation tasks and aggregation of results to ascertain if the outcomes lie within specified confidence intervals. Some other simulation models, such as the study of traffic conditions using simulations may need multiple instances to be executed for a number of different parameters. Cloud computing has opened up new avenues for individuals and organizations with limited resources to obtain answers to problems that hitherto required expensive and computationally-intensive resources. This paper presents SIMaaS, which is a cloudbased Simulation-as-a-Service to address these challenges. We demonstrate how lightweight solutions using Linux containers (e.g., Docker) are better suited to support such services instead of heavyweight hypervisor-based solutions, which are shown to incur substantial overhead in provisioning virtual machines on-demand. Empirical results validating our claims are presented in the context of two case studies.

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Section: Related Work On Cloud Resource Managementmentioning

confidence: 99%

A simulation as a service cloud middleware

et al. 2015

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“…Authors in [11] introduce a cost-efficient approach to select the most proper system (private or public cloud) to execute the workflow according to deadline constraint as well as cost saving. Li et al in [15] present a scheduling algorithm to schedule the application of large graph processing in consideration of both cost and schedule length.…”

Section: Related Workmentioning

confidence: 99%

A New Approach for Task Scheduling Optimization in Mobile Cloud Computing

Hung

Bui

Huh

2014

Lecture Notes in Electrical Engineering

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Mobile cloud computing is growing rapidly because its device (i.e., smart phone) is becoming one of the main processing devices for users nowadays. However, there are still some negative impacts that affect cloud access, especially when access to cloud becomes expensive but recent studies are not yet efficient in eliminating these. In this paper, we present an effective task scheduling by collaborating thick-thin clients and cloud to guarantee a better accessibility to cloud network and boost up the processing time in the mobile cloud platform while considering the network bandwidth and cost for cloud service usage. Intensive simulation proves that our method can improve the task scheduling efficiency and is better cost-effective than other works.

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“…To the best of our knowledge, previous work on VMA has been only experimental [18,29,32,37] or has focused on different cost functions [19,4,13,17]. First, we provide an overview of previous theoretical work for related assignment problems (storage allocation, scheduling, network design, etc.).…”

Section: Related Workmentioning

confidence: 99%

Power-Efficient Assignment of Virtual Machines to Physical Machines

Aroca

Anta

Mosteiro

et al. 2014

Adaptive Resource Management and Scheduling for Cloud Computing

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Abstract. Motivated by current trends in cloud computing, we study a version of the generalized assignment problem where a set of virtual processors has to be implemented by a set of identical processors. For literature consistency, we say that a set of virtual machines (VMs) is assigned to a set of physical machines (PMs). The optimization criteria is to minimize the power consumed by all the PMs. We term the problem Virtual Machine Assignment (VMA). Crucial differences with previous work include a variable number of PMs, that each VM must be assigned to exactly one PM (i.e., VMs cannot be implemented fractionally), and a minimum power consumption for each active PM. Such infrastructure may be strictly constrained in the number of PMs or in the PMs' capacity, depending on how costly (in terms of power consumption) it is to add a new PM to the system or to heavily load some of the existing PMs. Low usage or ample budget yields models where PM capacity and/or the number of PMs may be assumed unbounded for all practical purposes. We study four VMA problems depending on whether the capacity or the number of PMs is bounded or not. Specifically, we study hardness and online competitiveness for a variety of cases. To the best of our knowledge, this is the first comprehensive study of the VMA problem for this cost function.

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Cost-Efficient Scheduling Heuristics for Deadline Constrained Workloads on Hybrid Clouds

Cited by 74 publications

References 11 publications

A simulation as a service cloud middleware

A simulation as a service cloud middleware

A New Approach for Task Scheduling Optimization in Mobile Cloud Computing

Power-Efficient Assignment of Virtual Machines to Physical Machines

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