16,221 research outputs found
DYVERSE: DYnamic VERtical Scaling in Multi-tenant Edge Environments
Multi-tenancy in resource-constrained environments is a key challenge in Edge
computing. In this paper, we develop 'DYVERSE: DYnamic VERtical Scaling in
Edge' environments, which is the first light-weight and dynamic vertical
scaling mechanism for managing resources allocated to applications for
facilitating multi-tenancy in Edge environments. To enable dynamic vertical
scaling, one static and three dynamic priority management approaches that are
workload-aware, community-aware and system-aware, respectively are proposed.
This research advocates that dynamic vertical scaling and priority management
approaches reduce Service Level Objective (SLO) violation rates. An online-game
and a face detection workload in a Cloud-Edge test-bed are used to validate the
research. The merits of DYVERSE is that there is only a sub-second overhead per
Edge server when 32 Edge servers are deployed on a single Edge node. When
compared to executing applications on the Edge servers without dynamic vertical
scaling, static priorities and dynamic priorities reduce SLO violation rates of
requests by up to 4% and 12% for the online game, respectively, and in both
cases 6% for the face detection workload. Moreover, for both workloads, the
system-aware dynamic vertical scaling method effectively reduces the latency of
non-violated requests, when compared to other methods
Global Grids and Software Toolkits: A Study of Four Grid Middleware Technologies
Grid is an infrastructure that involves the integrated and collaborative use
of computers, networks, databases and scientific instruments owned and managed
by multiple organizations. Grid applications often involve large amounts of
data and/or computing resources that require secure resource sharing across
organizational boundaries. This makes Grid application management and
deployment a complex undertaking. Grid middlewares provide users with seamless
computing ability and uniform access to resources in the heterogeneous Grid
environment. Several software toolkits and systems have been developed, most of
which are results of academic research projects, all over the world. This
chapter will focus on four of these middlewares--UNICORE, Globus, Legion and
Gridbus. It also presents our implementation of a resource broker for UNICORE
as this functionality was not supported in it. A comparison of these systems on
the basis of the architecture, implementation model and several other features
is included.Comment: 19 pages, 10 figure
Software-Defined Cloud Computing: Architectural Elements and Open Challenges
The variety of existing cloud services creates a challenge for service
providers to enforce reasonable Software Level Agreements (SLA) stating the
Quality of Service (QoS) and penalties in case QoS is not achieved. To avoid
such penalties at the same time that the infrastructure operates with minimum
energy and resource wastage, constant monitoring and adaptation of the
infrastructure is needed. We refer to Software-Defined Cloud Computing, or
simply Software-Defined Clouds (SDC), as an approach for automating the process
of optimal cloud configuration by extending virtualization concept to all
resources in a data center. An SDC enables easy reconfiguration and adaptation
of physical resources in a cloud infrastructure, to better accommodate the
demand on QoS through a software that can describe and manage various aspects
comprising the cloud environment. In this paper, we present an architecture for
SDCs on data centers with emphasis on mobile cloud applications. We present an
evaluation, showcasing the potential of SDC in two use cases-QoS-aware
bandwidth allocation and bandwidth-aware, energy-efficient VM placement-and
discuss the research challenges and opportunities in this emerging area.Comment: Keynote Paper, 3rd International Conference on Advances in Computing,
Communications and Informatics (ICACCI 2014), September 24-27, 2014, Delhi,
Indi
SAMI: Service-Based Arbitrated Multi-Tier Infrastructure for Mobile Cloud Computing
Mobile Cloud Computing (MCC) is the state-ofthe- art mobile computing
technology aims to alleviate resource poverty of mobile devices. Recently,
several approaches and techniques have been proposed to augment mobile devices
by leveraging cloud computing. However, long-WAN latency and trust are still
two major issues in MCC that hinder its vision. In this paper, we analyze MCC
and discuss its issues. We leverage Service Oriented Architecture (SOA) to
propose an arbitrated multi-tier infrastructure model named SAMI for MCC. Our
architecture consists of three major layers, namely SOA, arbitrator, and
infrastructure. The main strength of this architecture is in its multi-tier
infrastructure layer which leverages infrastructures from three main sources of
Clouds, Mobile Network Operators (MNOs), and MNOs' authorized dealers. On top
of the infrastructure layer, an arbitrator layer is designed to classify
Services and allocate them the suitable resources based on several metrics such
as resource requirement, latency and security. Utilizing SAMI facilitate
development and deployment of service-based platform-neutral mobile
applications.Comment: 6 full pages, accepted for publication in IEEE MobiCC'12 conference,
MobiCC 2012:IEEE Workshop on Mobile Cloud Computing, Beijing, Chin
VirtFogSim: A parallel toolbox for dynamic energy-delay performance testing and optimization of 5G Mobile-Fog-Cloud virtualized platforms
It is expected that the pervasive deployment of multi-tier 5G-supported Mobile-Fog-Cloudtechnological computing platforms will constitute an effective means to support the real-time execution of future Internet applications by resource- and energy-limited mobile devices. Increasing interest in this emerging networking-computing technology demands the optimization and performance evaluation of several parts of the underlying infrastructures. However, field trials are challenging due to their operational costs, and in every case, the obtained results could be difficult to repeat and customize. These emergingMobile-Fog-Cloud ecosystems still lack, indeed, customizable software tools for the performance simulation of their computing-networking building blocks. Motivated by these considerations, in this contribution, we present VirtFogSim. It is aMATLAB-supported software toolbox that allows the dynamic joint optimization and tracking of the energy and delay performance of Mobile-Fog-Cloud systems for the execution of applications described by general Directed Application Graphs (DAGs). In a nutshell, the main peculiar features of the proposed VirtFogSim toolbox are that: (i) it allows the joint dynamic energy-aware optimization of the placement of the application tasks and the allocation of the needed computing-networking resources under hard constraints on acceptable overall execution times, (ii) it allows the repeatable and customizable simulation of the resulting energy-delay performance of the overall system; (iii) it allows the dynamic tracking of the performed resource allocation under time-varying operational environments, as those typically featuring mobile applications; (iv) it is equipped with a user-friendly Graphic User Interface (GUI) that supports a number of graphic formats for data rendering, and (v) itsMATLAB code is optimized for running atop multi-core parallel execution platforms. To check both the actual optimization and scalability capabilities of the VirtFogSim toolbox, a number of experimental setups featuring different use cases and operational environments are simulated, and their performances are compared
Technical Report: A Trace-Based Performance Study of Autoscaling Workloads of Workflows in Datacenters
To improve customer experience, datacenter operators offer support for
simplifying application and resource management. For example, running workloads
of workflows on behalf of customers is desirable, but requires increasingly
more sophisticated autoscaling policies, that is, policies that dynamically
provision resources for the customer. Although selecting and tuning autoscaling
policies is a challenging task for datacenter operators, so far relatively few
studies investigate the performance of autoscaling for workloads of workflows.
Complementing previous knowledge, in this work we propose the first
comprehensive performance study in the field. Using trace-based simulation, we
compare state-of-the-art autoscaling policies across multiple application
domains, workload arrival patterns (e.g., burstiness), and system utilization
levels. We further investigate the interplay between autoscaling and regular
allocation policies, and the complexity cost of autoscaling. Our quantitative
study focuses not only on traditional performance metrics and on
state-of-the-art elasticity metrics, but also on time- and memory-related
autoscaling-complexity metrics. Our main results give strong and quantitative
evidence about previously unreported operational behavior, for example, that
autoscaling policies perform differently across application domains and by how
much they differ.Comment: Technical Report for the CCGrid 2018 submission "A Trace-Based
Performance Study of Autoscaling Workloads of Workflows in Datacenters
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