9,465 research outputs found
Synchronized Multi-Load Balancer with Fault Tolerance in Cloud
In this method, service of one load balancer can be borrowed or shared among
other load balancers when any correction is needed in the estimation of the
load.Comment: 8 Pages, 10 figure
Memetic Multi-Objective Particle Swarm Optimization-Based Energy-Aware Virtual Network Embedding
In cloud infrastructure, accommodating multiple virtual networks on a single
physical network reduces power consumed by physical resources and minimizes
cost of operating cloud data centers. However, mapping multiple virtual network
resources to physical network components, called virtual network embedding
(VNE), is known to be NP-hard. With considering energy efficiency, the problem
becomes more complicated. In this paper, we model energy-aware virtual network
embedding, devise metrics for evaluating performance of energy aware virtual
network-embedding algorithms, and propose an energy aware virtual
network-embedding algorithm based on multi-objective particle swarm
optimization augmented with local search to speed up convergence of the
proposed algorithm and improve solutions quality. Performance of the proposed
algorithm is evaluated and compared with existing algorithms using extensive
simulations, which show that the proposed algorithm improves virtual network
embedding by increasing revenue and decreasing energy consumption.Comment: arXiv admin note: text overlap with arXiv:1504.0684
Multiple Workflows Scheduling in Multi-tenant Distributed Systems: A Taxonomy and Future Directions
The workflow is a general notion representing the automated processes along
with the flow of data. The automation ensures the processes being executed in
the order. Therefore, this feature attracts users from various background to
build the workflow. However, the computational requirements are enormous and
investing for a dedicated infrastructure for these workflows is not always
feasible. To cater to the broader needs, multi-tenant platforms for executing
workflows were began to be built. In this paper, we identify the problems and
challenges in the multiple workflows 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 workflows scheduling in multi-tenant distributed systems.Comment: Several changes has been done based on reviewers' comments after
first round review. This is a pre-print for paper (currently under second
round review) submitted to ACM Computing Survey
Reducing Electricity Demand Charge for Data Centers with Partial Execution
Data centers consume a large amount of energy and incur substantial
electricity cost. In this paper, we study the familiar problem of reducing data
center energy cost with two new perspectives. First, we find, through an
empirical study of contracts from electric utilities powering Google data
centers, that demand charge per kW for the maximum power used is a major
component of the total cost. Second, many services such as Web search tolerate
partial execution of the requests because the response quality is a concave
function of processing time. Data from Microsoft Bing search engine confirms
this observation.
We propose a simple idea of using partial execution to reduce the peak power
demand and energy cost of data centers. We systematically study the problem of
scheduling partial execution with stringent SLAs on response quality. For a
single data center, we derive an optimal algorithm to solve the workload
scheduling problem. In the case of multiple geo-distributed data centers, the
demand of each data center is controlled by the request routing algorithm,
which makes the problem much more involved. We decouple the two aspects, and
develop a distributed optimization algorithm to solve the large-scale request
routing problem. Trace-driven simulations show that partial execution reduces
cost by for one data center, and by for geo-distributed
data centers together with request routing.Comment: 12 page
Secure Cloud-Edge Deployments, with Trust
Assessing the security level of IoT applications to be deployed to
heterogeneous Cloud-Edge infrastructures operated by different providers is a
non-trivial task. In this article, we present a methodology that permits to
express security requirements for IoT applications, as well as infrastructure
security capabilities, in a simple and declarative manner, and to automatically
obtain an explainable assessment of the security level of the possible
application deployments. The methodology also considers the impact of trust
relations among different stakeholders using or managing Cloud-Edge
infrastructures. A lifelike example is used to showcase the prototyped
implementation of the methodology
Investigating Decision Support Techniques for Automating Cloud Service Selection
The compass of Cloud infrastructure services advances steadily leaving users
in the agony of choice. To be able to select the best mix of service offering
from an abundance of possibilities, users must consider complex dependencies
and heterogeneous sets of criteria. Therefore, we present a PhD thesis proposal
on investigating an intelligent decision support system for selecting Cloud
based infrastructure services (e.g. storage, network, CPU).Comment: Accepted by IEEE Cloudcom 2012 - PhD consortium trac
Bioinformatics Computational Cluster Batch Task Profiling with Machine Learning for Failure Prediction
Motivation: Traditional computational cluster schedulers are based on user
inputs and run time needs request for memory and CPU, not IO. Heavily IO bound
task run times, like ones seen in many big data and bioinformatics problems,
are dependent on the IO subsystems scheduling and are problematic for cluster
resource scheduling. The problematic rescheduling of IO intensive and errant
tasks is a lost resource. Understanding the conditions in both successful and
failed tasks and differentiating them could provide knowledge to enhancing
cluster scheduling and intelligent resource optimization.
Results: We analyze a production computational cluster contributing 6.7
thousand CPU hours to research over two years. Through this analysis we develop
a machine learning task profiling agent for clusters that attempts to predict
failures between identically provision requested tasks
A Survey on Online Judge Systems and Their Applications
Online judges are systems designed for the reliable evaluation of algorithm
source code submitted by users, which is next compiled and tested in a
homogeneous environment. Online judges are becoming popular in various
applications. Thus, we would like to review the state of the art for these
systems. We classify them according to their principal objectives into systems
supporting organization of competitive programming contests, enhancing
education and recruitment processes, facilitating the solving of data mining
challenges, online compilers and development platforms integrated as components
of other custom systems. Moreover, we introduce a formal definition of an
online judge system and summarize the common evaluation methodology supported
by such systems. Finally, we briefly discuss an Optil.io platform as an example
of an online judge system, which has been proposed for the solving of complex
optimization problems. We also analyze the competition results conducted using
this platform. The competition proved that online judge systems, strengthened
by crowdsourcing concepts, can be successfully applied to accurately and
efficiently solve complex industrial- and science-driven challenges.Comment: Authors pre-print of the article accepted for publication in ACM
Computing Surveys (accepted on 19-Sep-2017
Finding Faster Configurations using FLASH
Finding good configurations for a software system is often challenging since
the number of configuration options can be large. Software engineers often make
poor choices about configuration or, even worse, they usually use a sub-optimal
configuration in production, which leads to inadequate performance. To assist
engineers in finding the (near) optimal configuration, this paper introduces
FLASH, a sequential model-based method, which sequentially explores the
configuration space by reflecting on the configurations evaluated so far to
determine the next best configuration to explore. FLASH scales up to software
systems that defeat the prior state of the art model-based methods in this
area. FLASH runs much faster than existing methods and can solve both
single-objective and multi-objective optimization problems. The central insight
of this paper is to use the prior knowledge (gained from prior runs) to choose
the next promising configuration. This strategy reduces the effort (i.e.,
number of measurements) required to find the (near) optimal configuration. We
evaluate FLASH using 30 scenarios based on 7 software systems to demonstrate
that FLASH saves effort in 100% and 80% of cases in single-objective and
multi-objective problems respectively by up to several orders of magnitude
compared to the state of the art techniques
A survey of QoS-aware web service composition techniques
Web service composition can be briefly described as the process of aggregating services with disparate functionalities into a new composite service in order to meet increasingly complex needs of users. Service composition process has been accurate on dealing with services having disparate functionalities, however, over the years the number of web services in particular that exhibit similar functionalities and varying Quality of Service (QoS) has significantly increased. As such, the problem becomes how to select appropriate web services such that the QoS of the resulting composite service is maximized or, in some cases, minimized. This constitutes an NP-hard problem as it is complicated and difficult to solve. In this paper, a discussion of concepts of web service composition and a holistic review of current service composition techniques proposed in literature is presented. Our review spans several publications in the field that can serve as a road map for future research
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