45,503 research outputs found
Deadline-aware Power Management in Data Centers
We study the dynamic power optimization problem in data centers. We formulate
and solve the following offline problem: in which slot which server has to be
assigned to which job; and in which slot which server has to be switched ON or
OFF so that the total power is optimal for some time horizon. We show that the
offline problem is a new version of generalized assignment problem including
new constraints issuing from deadline characteristics of jobs and difference of
activation energy of servers. We propose an online algorithm that solves the
problem heuristically and compare it to randomized routing
Energy-Efficient Scheduling of HPC Applications in Cloud Computing Environments
The use of High Performance Computing (HPC) in commercial and consumer IT
applications is becoming popular. They need the ability to gain rapid and
scalable access to high-end computing capabilities. Cloud computing promises to
deliver such a computing infrastructure using data centers so that HPC users
can access applications and data from a Cloud anywhere in the world on demand
and pay based on what they use. However, the growing demand drastically
increases the energy consumption of data centers, which has become a critical
issue. High energy consumption not only translates to high energy cost, which
will reduce the profit margin of Cloud providers, but also high carbon
emissions which is not environmentally sustainable. Hence, energy-efficient
solutions are required that can address the high increase in the energy
consumption from the perspective of not only Cloud provider but also from the
environment. To address this issue we propose near-optimal scheduling policies
that exploits heterogeneity across multiple data centers for a Cloud provider.
We consider a number of energy efficiency factors such as energy cost, carbon
emission rate, workload, and CPU power efficiency which changes across
different data center depending on their location, architectural design, and
management system. Our carbon/energy based scheduling policies are able to
achieve on average up to 30% of energy savings in comparison to profit based
scheduling policies leading to higher profit and less carbon emissions
On Time-Sensitive Revenue Management and Energy Scheduling in Green Data Centers
In this paper, we design an analytically and experimentally better online
energy and job scheduling algorithm with the objective of maximizing net profit
for a service provider in green data centers. We first study the previously
known algorithms and conclude that these online algorithms have provable poor
performance against their worst-case scenarios. To guarantee an online
algorithm's performance in hindsight, we design a randomized algorithm to
schedule energy and jobs in the data centers and prove the algorithm's expected
competitive ratio in various settings. Our algorithm is theoretical-sound and
it outperforms the previously known algorithms in many settings using both real
traces and simulated data. An optimal offline algorithm is also implemented as
an empirical benchmark
Energy Efficient Geographical Load Balancing via Dynamic Deferral of Workload
With the increasing popularity of Cloud computing and Mobile computing,
individuals, enterprises and research centers have started outsourcing their IT
and computational needs to on-demand cloud services. Recently geographical load
balancing techniques have been suggested for data centers hosting cloud
computation in order to reduce energy cost by exploiting the electricity price
differences across regions. However, these algorithms do not draw distinction
among diverse requirements for responsiveness across various workloads. In this
paper, we use the flexibility from the Service Level Agreements (SLAs) to
differentiate among workloads under bounded latency requirements and propose a
novel approach for cost savings for geographical load balancing. We investigate
how much workload to be executed in each data center and how much workload to
be delayed and migrated to other data centers for energy saving while meeting
deadlines. We present an offline formulation for geographical load balancing
problem with dynamic deferral and give online algorithms to determine the
assignment of workload to the data centers and the migration of workload
between data centers in order to adapt with dynamic electricity price changes.
We compare our algorithms with the greedy approach and show that significant
cost savings can be achieved by migration of workload and dynamic deferral with
future electricity price prediction. We validate our algorithms on MapReduce
traces and show that geographic load balancing with dynamic deferral can
provide 20-30% cost-savings.Comment: 10 pages, 6 figure
Profit Maximization for Geographical Dispersed Green Data Centers
This paper aims at maximizing the profit associated with running
geographically dispersed green data centers, which offer multiple classes of
service. To this end, we formulate an optimization framework which relies on
the accuracy of the G/D/1 queue in characterizing the workload distribution,
and taps on the merits of the workload decomposition into green and brown
workload served by green and brown energy resources. Moreover, we take into
account of not only the Service Level Agreements (SLAs) between the data
centers and clients but also different deregulated electricity markets of data
centers located at different regions. We prove the convexity of our
optimization problem and the performance of the proposed workload distribution
strategy is evaluated via simulations
Resource Management and Scheduling for Big Data Applications in Cloud Computing Environments
This chapter presents software architectures of the big data processing
platforms. It will provide an in-depth knowledge on resource management
techniques involved while deploying big data processing systems on cloud
environment. It starts from the very basics and gradually introduce the core
components of resource management which we have divided in multiple layers. It
covers the state-of-art practices and researches done in SLA-based resource
management with a specific focus on the job scheduling mechanisms.Comment: 27 pages, 9 figure
A Taxonomy and Future Directions for Sustainable Cloud Computing: 360 Degree View
The cloud computing paradigm offers on-demand services over the Internet and
supports a wide variety of applications. With the recent growth of Internet of
Things (IoT) based applications the usage of cloud services is increasing
exponentially. The next generation of cloud computing must be energy-efficient
and sustainable to fulfil the end-user requirements which are changing
dynamically. Presently, cloud providers are facing challenges to ensure the
energy efficiency and sustainability of their services. The usage of large
number of cloud datacenters increases cost as well as carbon footprints, which
further effects the sustainability of cloud services. In this paper, we propose
a comprehensive taxonomy of sustainable cloud computing. The taxonomy is used
to investigate the existing techniques for sustainability that need careful
attention and investigation as proposed by several academic and industry
groups. Further, the current research on sustainable cloud computing is
organized into several categories: application design, sustainability metrics,
capacity planning, energy management, virtualization, thermal-aware scheduling,
cooling management, renewable energy and waste heat utilization. The existing
techniques have been compared and categorized based on the common
characteristics and properties. A conceptual model for sustainable cloud
computing has been proposed along with discussion on future research
directions.Comment: 68 pages, 38 figures, ACM Computing Surveys, 201
Decentralized Edge-to-Cloud Load-balancing: Service Placement for the Internet of Things
Internet of Things (IoT) requires a new processing paradigm that inherits the
scalability of the cloud while minimizing network latency using resources
closer to the network edge. Building up such flexibility within the
edge-to-cloud continuum consisting of a distributed networked ecosystem of
heterogeneous computing resources is challenging. Load-balancing for fog
computing becomes a cornerstone for cost-effective system management and
operations. This paper studies two optimization objectives and formulates a
decentralized load-balancing problem for IoT service placement: (global) IoT
workload balance and (local) quality of service, in terms of minimizing the
cost of deadline violation, service deployment, and unhosted services. The
proposed solution, EPOS Fog, introduces a decentralized multiagent system for
collective learning that utilizes edge-to-cloud nodes to jointly balance the
input workload across the network and minimize the costs involved in service
execution. The agents locally generate possible assignments of requests to
resources and then cooperatively select an assignment such that their
combination maximizes edge utilization while minimizes service execution cost.
Extensive experimental evaluation with realistic Google cluster workloads on
various networks demonstrates the superior performance of EPOS Fog in terms of
workload balance and quality of service, compared to approaches such as First
Fit and exclusively Cloud-based. The findings demonstrate how distributed
computational resources on the edge can be utilized more cost-effectively by
harvesting collective intelligence.Comment: 16 pages and 15 figure
Performance Constraint and Power-Aware Allocation For User Requests In Virtual Computing Lab
Cloud computing is driven by economies of scale. A cloud system uses
virtualization technology to provide cloud resources (e.g. CPU, memory) to
users in form of virtual machines. Virtual machine (VM), which is a sandbox for
user application, fits well in the education environment to provide
computational resources for teaching and research needs. In resource
management, they want to reduce costs in operations by reducing expensive cost
of electronic bill of large-scale data center system. A lease-based model is
suitable for our Virtual Computing Lab, in which users ask resources on a lease
of virtual machines. This paper proposes two host selection policies, named MAP
(minimum of active physical hosts) and MAP-H2L, and four algorithms solving the
lease scheduling problem. FF-MAP, FF-MAP-H2L algorithms meet a trade-off
between the energy consumption and Quality of Service (e.g. performance). The
simulation on 7-day workload, which converted from LLNL Atlas log, showed the
FF-MAP and FF-MAP-H2L algorithms reducing 7.24% and 7.42% energy consumption
than existing greedy mapping algorithm in the leasing scheduler Haizea. In
addition, we introduce a ratio \theta of consolidation in HalfPI-FF-MAP and
PI-FF-MAP algorithms, in which \theta is \pi/2 and \pi, and results on their
simulations show that energy consumption decreased by 34.87% and 63.12%
respectively.Comment: 10 page
PowerTracer: Tracing requests in multi-tier services to save cluster power consumption
As energy proportional computing gradually extends the success of DVFS
(Dynamic voltage and frequency scaling) to the entire system, DVFS control
algorithms will play a key role in reducing server clusters' power consumption.
The focus of this paper is to provide accurate cluster-level DVFS control for
power saving in a server cluster. To achieve this goal, we propose a request
tracing approach that online classifies the major causal path patterns of a
multi-tier service and monitors their performance data as a guide for accurate
DVFS control. The request tracing approach significantly decreases the time
cost of performance profiling experiments that aim to establish the empirical
performance model. Moreover, it decreases the controller complexity so that we
can introduce a much simpler feedback controller, which only relies on the
single-node DVFS modulation at a time as opposed to varying multiple CPU
frequencies simultaneously. Based on the request tracing approach, we present a
hybrid DVFS control system that combines an empirical performance model for
fast modulation at different load levels and a simpler feedback controller for
adaption. We implement a prototype of the proposed system, called PowerTracer,
and conduct extensive experiments on a 3-tier platform. Our experimental
results show that PowerTracer outperforms its peer in terms of power saving and
system performance.Comment: 10 pages, 22 figure
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