718 research outputs found
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
Abstract Interpretation of Stateful Networks
Modern networks achieve robustness and scalability by maintaining states on
their nodes. These nodes are referred to as middleboxes and are essential for
network functionality. However, the presence of middleboxes drastically
complicates the task of network verification. Previous work showed that the
problem is undecidable in general and EXPSPACE-complete when abstracting away
the order of packet arrival.
We describe a new algorithm for conservatively checking isolation properties
of stateful networks. The asymptotic complexity of the algorithm is polynomial
in the size of the network, albeit being exponential in the maximal number of
queries of the local state that a middlebox can do, which is often small.
Our algorithm is sound, i.e., it can never miss a violation of safety but may
fail to verify some properties. The algorithm performs on-the fly abstract
interpretation by (1) abstracting away the order of packet processing and the
number of times each packet arrives, (2) abstracting away correlations between
states of different middleboxes and channel contents, and (3) representing
middlebox states by their effect on each packet separately, rather than taking
into account the entire state space. We show that the abstractions do not lose
precision when middleboxes may reset in any state. This is encouraging since
many real middleboxes reset, e.g., after some session timeout is reached or due
to hardware failure
Proceedings of the 3rd Workshop on Domain-Specific Language Design and Implementation (DSLDI 2015)
The goal of the DSLDI workshop is to bring together researchers and
practitioners interested in sharing ideas on how DSLs should be designed,
implemented, supported by tools, and applied in realistic application contexts.
We are both interested in discovering how already known domains such as graph
processing or machine learning can be best supported by DSLs, but also in
exploring new domains that could be targeted by DSLs. More generally, we are
interested in building a community that can drive forward the development of
modern DSLs. These informal post-proceedings contain the submitted talk
abstracts to the 3rd DSLDI workshop (DSLDI'15), and a summary of the panel
discussion on Language Composition
- …