6,709 research outputs found
A Self-adaptive Agent-based System for Cloud Platforms
Cloud computing is a model for enabling on-demand network access to a shared
pool of computing resources, that can be dynamically allocated and released
with minimal effort. However, this task can be complex in highly dynamic
environments with various resources to allocate for an increasing number of
different users requirements. In this work, we propose a Cloud architecture
based on a multi-agent system exhibiting a self-adaptive behavior to address
the dynamic resource allocation. This self-adaptive system follows a MAPE-K
approach to reason and act, according to QoS, Cloud service information, and
propagated run-time information, to detect QoS degradation and make better
resource allocation decisions. We validate our proposed Cloud architecture by
simulation. Results show that it can properly allocate resources to reduce
energy consumption, while satisfying the users demanded QoS
Service Level Agreement-based GDPR Compliance and Security assurance in (multi)Cloud-based systems
Compliance with the new European General Data Protection Regulation (Regulation (EU) 2016/679) and security
assurance are currently two major challenges of Cloud-based systems. GDPR compliance implies both privacy and security
mechanisms definition, enforcement and control, including evidence collection. This paper presents a novel DevOps
framework aimed at supporting Cloud consumers in designing, deploying and operating (multi)Cloud systems that include
the necessary privacy and security controls for ensuring transparency to end-users, third parties in service provision (if any)
and law enforcement authorities. The framework relies on the risk-driven specification at design time of privacy and security
level objectives in the system Service Level Agreement (SLA) and in their continuous monitoring and enforcement at runtime.The research leading to these results has received
funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 644429
and No 780351, MUSA project and ENACT project,
respectively. We would also like to acknowledge all the
members of the MUSA Consortium and ENACT Consortium
for their valuable help
MorphoSys: efficient colocation of QoS-constrained workloads in the cloud
In hosting environments such as IaaS clouds, desirable application performance is usually guaranteed through the use of Service Level Agreements (SLAs), which specify minimal fractions of resource capacities that must be allocated for unencumbered use for proper operation. Arbitrary colocation of applications with different SLAs on a single host may result in inefficient utilization of the host’s resources. In this paper, we propose that periodic resource allocation and consumption models -- often used to characterize real-time workloads -- be used for a more granular expression of SLAs. Our proposed SLA model has the salient feature that it exposes flexibilities that enable the infrastructure provider to safely transform SLAs from one form to another for the purpose of achieving more efficient colocation. Towards that goal, we present MORPHOSYS: a framework for a service that allows the manipulation of SLAs to enable efficient colocation of arbitrary workloads in a dynamic setting. We present results from extensive trace-driven simulations of colocated Video-on-Demand servers in a cloud setting. These results show that potentially-significant reduction in wasted resources (by as much as 60%) are possible using MORPHOSYS.National Science Foundation (0720604, 0735974, 0820138, 0952145, 1012798
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