4,749 research outputs found
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
Cloud computing resource scheduling and a survey of its evolutionary approaches
A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon
Message passing resource allocation for the uplink of multicarrier systems
We propose a novel distributed resource allocation scheme for the up-link of
a cellular multi-carrier system based on the message passing (MP) algorithm. In
the proposed approach each transmitter iteratively sends and receives
information messages to/from the base station with the goal of achieving an
optimal resource allocation strategy. The exchanged messages are the solution
of small distributed allocation problems. To reduce the computational load, the
MP problems at the terminals follow a dynamic programming formulation. The
advantage of the proposed scheme is that it distributes the computational
effort among all the transmitters in the cell and it does not require the
presence of a central controller that takes all the decisions. Numerical
results show that the proposed approach is an excellent solution to the
resource allocation problem for cellular multi-carrier systems.Comment: 6 pages, 4 figure
Resilient Distributed Optimization Algorithms for Resource Allocation
Distributed algorithms provide flexibility over centralized algorithms for
resource allocation problems, e.g., cyber-physical systems. However, the
distributed nature of these algorithms often makes the systems susceptible to
man-in-the-middle attacks, especially when messages are transmitted between
price-taking agents and a central coordinator. We propose a resilient strategy
for distributed algorithms under the framework of primal-dual distributed
optimization. We formulate a robust optimization model that accounts for
Byzantine attacks on the communication channels between agents and coordinator.
We propose a resilient primal-dual algorithm using state-of-the-art robust
statistics methods. The proposed algorithm is shown to converge to a
neighborhood of the robust optimization model, where the neighborhood's radius
is proportional to the fraction of attacked channels.Comment: 15 pages, 1 figure, accepted to CDC 201
Market-Based Task Allocation Mechanisms for Limited Capacity Suppliers
This paper reports on the design and comparison of two economically-inspired mechanisms for task allocation in environments where sellers have finite production capacities and a cost structure composed of a fixed overhead cost and a constant marginal cost. Such mechanisms are required when a system consists of multiple self-interested stakeholders that each possess private information that is relevant to solving a system-wide problem. Against this background, we first develop a computationally tractable centralised mechanism that finds the set of producers that have the lowest total cost in providing a certain demand (i.e. it is efficient). We achieve this by extending the standard Vickrey-Clarke-Groves mechanism to allow for multi-attribute bids and by introducing a novel penalty scheme such that producers are incentivised to truthfully report their capacities and their costs. Furthermore our extended mechanism is able to handle sellers' uncertainty about their production capacity and ensures that individual agents find it profitable to participate in the mechanism. However, since this first mechanism is centralised, we also develop a complementary decentralised mechanism based around the continuous double auction. Again because of the characteristics of our domain, we need to extend the standard form of this protocol by introducing a novel clearing rule based around an order book. With this modified protocol, we empirically demonstrate (with simple trading strategies) that the mechanism achieves high efficiency. In particular, despite this simplicity, the traders can still derive a profit from the market which makes our mechanism attractive since these results are a likely lower bound on their expected returns
Problems related to the integration of fault tolerant aircraft electronic systems
Problems related to the design of the hardware for an integrated aircraft electronic system are considered. Taxonomies of concurrent systems are reviewed and a new taxonomy is proposed. An informal methodology intended to identify feasible regions of the taxonomic design space is described. Specific tools are recommended for use in the methodology. Based on the methodology, a preliminary strawman integrated fault tolerant aircraft electronic system is proposed. Next, problems related to the programming and control of inegrated aircraft electronic systems are discussed. Issues of system resource management, including the scheduling and allocation of real time periodic tasks in a multiprocessor environment, are treated in detail. The role of software design in integrated fault tolerant aircraft electronic systems is discussed. Conclusions and recommendations for further work are included
Distributed Time-Sensitive Task Selection in Mobile Crowdsensing
With the rich set of embedded sensors installed in smartphones and the large
number of mobile users, we witness the emergence of many innovative commercial
mobile crowdsensing applications that combine the power of mobile technology
with crowdsourcing to deliver time-sensitive and location-dependent information
to their customers. Motivated by these real-world applications, we consider the
task selection problem for heterogeneous users with different initial
locations, movement costs, movement speeds, and reputation levels. Computing
the social surplus maximization task allocation turns out to be an NP-hard
problem. Hence we focus on the distributed case, and propose an asynchronous
and distributed task selection (ADTS) algorithm to help the users plan their
task selections on their own. We prove the convergence of the algorithm, and
further characterize the computation time for users' updates in the algorithm.
Simulation results suggest that the ADTS scheme achieves the highest Jain's
fairness index and coverage comparing with several benchmark algorithms, while
yielding similar user payoff to a greedy centralized benchmark. Finally, we
illustrate how mobile users coordinate under the ADTS scheme based on some
practical movement time data derived from Google Maps
Completion-Time-Driven Scheduling for Uplink NOMA-Enabled Wireless Networks
Efficient scheduling policy is crucial in wireless
networks due to delay-sensitivity of many emerging applications.
In this work, we consider a joint user pairing and scheduling
(UPaS) scheme for multi-carrier non-orthogonal multiple access
(MC-NOMA)-enabled wireless networks to reduce the maximum
completion time of serving uplink users. The NOMA scheduling
problem is shown to be NP-hard and a shortest processing time
(SPT)-based strategy to solve the same problem within affordable
time and complexity is introduced. The simulation results confirm
the efficacy of the proposed scheduling scheme in terms of
the maximum completion time in comparison with orthogonal
multiple access (OMA) and random NOMA pairing
- …